In the tabs above, we have produced shortlists of example measures in a range of categories.
In the turquoise tabs, we highlight the measures for person centred coordinated care - “P3C” measures. First, there is a tab for “generic PC3” measures – i.e. measures that have a good coverage of most person centred domains. Next, we have tabs in different domains of person centred care, in categories that correspond to the National Voices “I” Statements.
In the green tabs, we highlight some diseases-specific measures for a variety of Long Term Conditions and End of Life. These shortlists include both P3C and QoL measures.
We conducted an in-depth scoping exercise of P3C measures for NHS England. Part of this process involved mapping all the questions from 66 shortlisted measures onto domains of person-centred care (as defined by the National Voices “I” statements). This process enabled us to identify a small sub-set of measures that could provide broad coverage of most of the P3C domains. Descriptions of these measures are provided below.
P3CEQ – the person centred coordinated care experience questionnaire
The P3CEQ is the most modern of the broad-coverage P3C measures. It is a modified version of the LTC6, which was modified in 2015 to cover domains of Person Centred Care and Coordination. This involved adding additional question for evolving healthcare concepts such as coordination and the use of personalised care plans. It was developed through extensive stakeholder engagement with patients, commissioners and practitioners who chose this measure to develop from a shortlist of identified measures.
Despite the very broad coverage, the measure is concise and efficient. It probes most domains of P3C in 11 questions (with the only exceptions being continuity of care and consistency of care). However if this measure is used with the same cohort over time, continuity can be explored through the combined construct of the tool. Face & Content validity have been established but not yet published. The measure has received encouraging feedback from patients, practitioners and researchers. It is also currently being translated into a number of different versions.
The most widely used measure of care transition quality is the Care Transitions Measure (CTM-15) (Coleman et al. 2002). The measure was designed to be used in a hospital setting. There is also a 3-item version available. It is comprised of 15 items that cover four “transition domains”, derived from patient focus groups. These four domains are: (1) Information Transfer, (2) Patient and Caregiver Preparation, (3) Support for Self-Management, and (4) Empowerment to Assert Preferences. The measure provides good coverage of a variety of aspects of person-centredness, with only the exception of single point of contact/key worker and therapeutic relationship.
However, poor psychometric properties have been reported. An independent evaluation revealed that the CTM-15 had good internal consistency (Cronbach's α=0.95) but demonstrated acquiescence bias (8.7% participants responded “Strongly agree” and 19% responded “Agree” to all items) and limited score variability (Anatchkova et al. 2014). Also, due to the wording of many of the questions, the CTM-15 may not be suitable for settings outside of the hospital environment.
PACIC -Patient Assessment of Chronic Illness Care
The PACIC (Glasgow et al. 2005) is a short, well-established tool for measuring patient experience of chronic illness care and should be sufficiently brief to use in many settings. It was developed in the US, based on the influential Chronic Care Model (CCM). It has 20 items that provide good coverage of a high number of important domains, including patient activation; delivery system design and decision support; goal setting and tailoring; problem-solving and contextual counselling; follow-up and coordination. However, it does not tap carer involvement, single point of contact/case manager and consistency of contact.
It has been extensively used in several countries and has been translated in to many different languages. The instrument is appropriate across a variety of chronic conditions because the questions are not tied to a specific type of chronic disease. However, up until 2012, there was little evidence surrounding its performance in UK settings. An evaluation of the psychometric qualities of the PACIC in a large sample of UK patients with long-term conditions sought to address this gap within the literature (Rick et al. 2012). This study reported that the PACIC scale had demonstrated potential utility for improving care for long-term conditions, but further assessment was necessary in order to ascertain why there were low levels of completion and to explore how effective the scale was at predicting outcomes and assessing the effects of interventions.
Face, construct, and concurrent validity, as well as measurement performance were demonstrated, characterising the PACIC as a reliable instrument. Test-re-test reliability was moderately stable over a three month interval. Most items strongly related to their retrospective subscale(s) and the overall model had moderate goodness of fit. It is also available as a longer 26 items (PACIC+) and translated versions. It has also been applied to diabetic patient populations.
PPE-15 - Picker Patient Experience Questionnaire
The PPE-15 was originally designed for use in inpatient care settings (Jenkinson 2002). It can be used for both planned and emergency inpatient settings, and was developed to identify patient experiences and problems with specific health care processes that affect the quality of care in inpatient settings. It contains specific questions about whether specific processes and events occurred during the patient’s care episode.
Whilst it has good, broad coverage of P3C domains, it does not include items for goal setting, single point, case manager, continuity of care, consistency of care and knowledge. As such, it is the weakest of the “generic” P3C measures in this list. Instead, it is a “classic” experience measure where some questions target system paternalism (e.g. “Did doctors talk in front of you as if you weren’t there?”). In contrast, modern alternatives such as the P3CEQ are designed to measure tangible aspects of recent developments in coordinated care such as care plans.
The PPE-15 has been independently evaluated as having good psychometric properties in a systematic review (Beattie et al. 2014). It is free to use, widely available, there are many translations available, and the questions can be incorporated into other inpatient surveys as part of routine data collection, allowing the comparison of scores over time. Such additional questions could include locally relevant questions, e.g., mode of admission, demographics questions and condition-specific questions. It has been widely translated.
PAIEC - Patient assessment of integrated elderly care
The PAIEC is a recently modified version of the PACIC specifically designed for older populations (Uittenbroek et al. 2015). Similar to the PACIC, it has good coverage of a high number of important domains, including patient activation; delivery system design and decision support; goal setting and tailoring; problem-solving and contextual counselling; follow-up and coordination. However, it does not tap carer involvement, single point of contact/case manager and consistency of contact. The initial publication/validation reveals it to be a valid measurement instrument that evaluates quality of integrated care and support from the perspective of elderly people (Uittenbroek et al. 2015).
Recently, a pair of PREMs have been designed specifically to evaluate the delivery of person-centred care for older people in intermediate care services – the IC-PREM-Home is designed for home services, the second (IC-PREM-Bed; see below) is designed for bed-based services. They have been developed and validated in the UK context (Teale and Young 2015) as part of the National Audit of Intermediate Care (NAIC). The tools were co-designed with a Delphi panel of experts and patients, with questions being modified to correspond with National Voices “I” statements. The IC-PREMs has been utilised across over 250 IC services audited by the NAIC. Although return rates were low (28% for the bed-based; 13% for the home IC-PREM), the bed-based rates are not dissimilar to other national surveys. The PREMs are designed to be used across a range of IC services for the purpose of local service improvement, rather than between-service comparisons.
A partner to the above mentioned IC-PREM-Home, the IC-PREM-Bed is a short measure that targets person-centred experience of bed-based intermediate care services for older people. Both measures are highlighted as ideal tools for use in the UK context.
Anatchkova, M. D., C. M. Barysauskas, R. L. Kinney, C. I. Kiefe, A. S. Ash, L. Lombardini, and J. J. Allison. 2014. “Psychometric Evaluation of the Care Transition Measure in TRACE-CORE: Do We Need a Better Measure?” Journal of the American Heart Association 3 (3): e001053–e001053. doi:10.1161/JAHA.114.001053.
Beattie, Michelle, William Lauder, Iain Atherton, and Douglas J. Murphy. 2014. “Instruments to Measure Patient Experience of Health Care Quality in Hospitals: A Systematic Review Protocol.” Systematic Reviews 3: 4. doi:10.1186/2046-4053-3-4.
Coleman, Eric A., Jodi D. Smith, Janet C. Frank, Theresa B. Eilertsen, Jill N. Thiare, and Andrew M. Kramer. 2002. “Development and Testing of a Measure Designed to Assess the Quality of Care Transitions.” International Journal of Integrated Care 2: e02.
Glasgow, Russell E., Edward H. Wagner, Judith Schaefer, Lisa D. Mahoney, Robert J. Reid, and Sarah M. Greene. 2005. “Development and Validation of the Patient Assessment of Chronic Illness Care (PACIC).” Medical Care 43 (5): 436–44.
Jenkinson, C. 2002. “The Picker Patient Experience Questionnaire: Development and Validation Using Data from in-Patient Surveys in Five Countries.” International Journal for Quality in Health Care 14 (5): 353–58. doi:10.1093/intqhc/14.5.353.
Rick, Jo, Kelly Rowe, Mark Hann, Bonnie Sibbald, David Reeves, Martin Roland, and Peter Bower. 2012. “Psychometric Properties of the Patient Assessment Of Chronic Illness Care Measure: Acceptability, Reliability and Validity in United Kingdom Patients with Long-Term Conditions.” BMC Health Services Research 12: 293. doi:10.1186/1472-6963-12-293.
Teale, E. A., and J. B. Young. 2015. “A Patient Reported Experience Measure (PREM) for Use by Older People in Community Services.” Age and Ageing 44 (4): 667–72. doi:10.1093/ageing/afv014.
Uittenbroek, Ronald J., Sijmen A. Reijneveld, Roy E. Stewart, Sophie L. W. Spoorenberg, Hubertus P. H. Kremer, and Klaske Wynia. 2015. “Development and Psychometric Evaluation of a Measure to Evaluate the Quality of Integrated Care: The Patient Assessment of Integrated Elderly Care.” Health Expectations: An International Journal of Public Participation in Health Care and Health Policy, July. doi:10.1111/hex.12391.
To identify our shortlist of P3C measures for the domain of “my goals & outcomes” we first scoped for existing measures, followed by a pragmatic shortlisting process – see the about page for more information. We then mapped the items on the shortlisted questionnaires to domains from the national voices “I” statements. Below, we discuss the questionnaires that had good coverage of the domain “my goals & outcomes”
There was one measure that covered all the sub-categories within this domain:
This measure is repeatedly suggested as a good overall domain measure throughout these P3C sections, as it provides the most extensive coverage of P3C overall. It is discussed more fully in the “Broad measures of P3C” tab above.
Other measures that covered substantial (but not complete) coverage of this domain were:
Patient Assessment of Care for Chronic Conditions (PACIC)
Patient Assessment of Integrated Elderly Care (PAIEC)
Both are discussed more fully in the “Broad measures of P3C” tab above.
Our PenCLAHRC framework of P3C is a nuanced construct that breaks down each of the “I” statement domains into sub-categories. These sub-categories are the real “nuts & bolts” of how these elements of P3C are operationalised in real-world settings. For “my goals & outcomes”, this is divided into sub-categories of
(a) Self-management
(b) Goal setting
(c) Empowerment/activation
(d) Carer Involvement
Below, we briefly discuss those measures that best mapped to these sub-domains of PCCC:
The Self-Efficacy Scale for chronic disease 6 items scale (SEM-CD -6)
The stand-out measure for this sub-category of this domain was the Self-Efficacy Scale for chronic disease 6 items scale (SEM-CD -6). This offered the most comprehensive coverage. Moreover, it was delivered in a short and efficient 6-item questionnaire. This measure is not condition specific, has been frequently used and widely cited. It was developed and validated by the Stanford Patient Education Centre. Evolved from several scales used in a chronic disease management study by (Lorig et al. 2001). It covers several domains that are common to a several types of chronic disease such as symptom control, role function, emotional functioning and communication with health care practitioners. Each of the 6 items are rated from 1 (not at all confident) to 10 (totally confident). The overall score is derived from the mean of the 6 items, with higher scores indicating improved self-efficacy.
The two measures that we are provided as an example for this sub-category are the PACIC and the PAIEC. Both these measures (as well as others not mentioned here) had items that could, and were, mapped to the P3C act of goal setting. As good measures of many aspects of P3C, they are described in more detail in the “Broad measures of P3C” tab above.
Another type of measure that is relevant to this sub-domain is iPROMs (see tab above for more detail).
There were a number of measures that covered this sub-category. Some examples are provided below.
The Patient Activation Measure (PAM)
One of the best known person-centred tools, the PAM (J. H. Hibbard et al. 2004; J. H. Hibbard et al. 2005)is a tool that measures how engaged a patient is in their healthcare by assessing their knowledge, skill and confidence for self-management (Dixon, Hibbard, and Tusler 2009). Unlike other measures of engagement, the PAM not only captures the patient’s beliefs about their ability to self-manage, but also the likelihood that they will act on these beliefs (J. Hibbard and Gilburt 2014). The PAM has been used in several countries, including the UK, where the NHS has recently agreed a large-scale, 5-year licence of the PAM.
There is a long (22 items) and a short version (13 items), both forms have been validated (J. H. Hibbard et al. 2004; J. H. Hibbard et al. 2005) and the creators state that they have good psychometric properties, which suggests they can be used to tailor individual interventions and capture change (Hibbard et al., 2004). In each version patients are asked to rate the degree to which they agree or disagree with a statement, with scores combined into a single ‘activation’ score from 0-100. This score represents how ‘activated’ the patient perceives themselves to be in the management of their health care. The score places them in one of four sub-groups that represent various levels of activation, ranging from low to high (J. Hibbard and Gilburt 2014).
The PAM has can be deployed in a number of ways - as a means of intervening to improve individual’s’ engagement and outcomes; to segment populations, and carry out risk stratification and to measure the performance of health care systems with the goal of evaluating the effectiveness of interventions (Greene and Hibbard 2012).
Whilst the measure has been well utilised, our own patient involvement groups and implementation interviews (that we held as part of this project) did flag some design and implementation issues concerning its use in a UK context. These included (1) the use of American phrases (2) the assumption of a certain lifestyle standard and (3) it does not allow for variations in activation for different aspects of health care.
The Health Care Empowerment Questionnaire (HCEQ)
This Canadian tool measures the extent of individual empowerment in relation to personal health care and services. It was created by identifying individual empowerment and indicators from the literature, devising related items and then testing them with a sample of older people (Gagnon et al. 2006).
Modified Perceived Involvement in Care Scale (MPICS)
M-PICS is a modified version of the Perceived Involvement in Care Scale. It revised the original measure by augmenting it with items that were pain specific as well as with questions that covered patient perceptions of the physician's control of the information-exchange process. The PICS assesses three domains of activities during the medical encounter: 1) doctor facilitation of patient involvement (five items); 2) patient information provision (PI) (four items); and 3) patient participation in decision making (four items) (Smith et al. 2006).
Patient Approach and Views toward Healthcare Communication Scale (PAV-COM)
The PAV-COM was designed in the US and initial publications reported that it is a valid tool for assessing patient approaches and views toward communication with physicians. This measure can be used to evaluate interventions to improve patient participation during healthcare encounters. It has been utilised in the US with older people (Tarn, Young, and Craig 2012).
No measure within the collection of shortlisted P3C measures substantially focused on this sub-domain. Measures that did include questions about this area of P3C included the:
The Health Care Empowerment Questionnaire (HECQ) - a brief description of this measure is provided above.
Picker Patient Experience Questionnaire (PPE-15) – a measure of many aspects of P3C, the PPE-15 is discussed in more detail in the “Broad measures of P3C” tab above.
Care Transitions Measure (CTM-15) – a measure of many aspects of P3C, the CTM is discussed in more detail in the “Broad measures of P3C” tab above.
Matched pair instrument (MPI) – A measure that aims targets patient/practioner communication, this measure is discussed in the “P3C measures for Information and Communication” above.
Patient participation in rehabilitation Questionnaire
A tool that measures patients’ experiences of participation in care and rehabilitation, it is discussed in the “Care Planning” tab above.
Dixon, Anna, Judith Hibbard, and Martin Tusler. 2009. “How Do People with Different Levels of Activation Self-Manage Their Chronic Conditions?:” The Patient: Patient-Centered Outcomes Research 2 (4): 257–68. doi:10.2165/11313790-000000000-00000.
Gagnon, Maxime, Réjean Hibert, Micheline Dubé, and Marie-France Dubois. 2006. “Development and Validation of an Instrument Measuring Individual Empowerment in Relation to Personal Health Care: The Health Care Empowerment Questionnaire (HCEQ).” American Journal of Health Promotion: AJHP 20 (6): 429–35.
Greene, Jessica, and Judith H. Hibbard. 2012. “Why Does Patient Activation Matter? An Examination of the Relationships Between Patient Activation and Health-Related Outcomes.” Journal of General Internal Medicine 27 (5): 520–26. doi:10.1007/s11606-011-1931-2.
Hibbard, Judith, and Helen Gilburt. 2014. “Supporting People to Manage Their Health.” An Introduction to Patient Activation. London: The King’s Fund. http://www.mylifeplus.org.uk/wp-content/uploads/2014/09/supporting-people-manage-health-patient-activation-may14.pdf.
Hibbard, Judith H., Eldon R. Mahoney, Jean Stockard, and Martin Tusler. 2005. “Development and Testing of a Short Form of the Patient Activation Measure.” Health Services Research 40 (6 Pt 1): 1918–30. doi:10.1111/j.1475-6773.2005.00438.x.
Hibbard, Judith H., Jean Stockard, Eldon R. Mahoney, and Martin Tusler. 2004. “Development of the Patient Activation Measure (PAM): Conceptualizing and Measuring Activation in Patients and Consumers: Development of the Patient Activation Measure (PAM).” Health Services Research 39 (4p1): 1005–26. doi:10.1111/j.1475-6773.2004.00269.x.
Lorig, K. R., D. S. Sobel, P. L. Ritter, D. Laurent, and M. Hobbs. 2001. “Effect of a Self-Management Program on Patients with Chronic Disease.” Effective Clinical Practice: ECP 4 (6): 256–62.
Smith, Meredith Y., Gary Winkel, Jennifer Egert, Mariana Diaz-Wionczek, and Katherine N. DuHamel. 2006. “Patient-Physician Communication in the Context of Persistent Pain: Validation of a Modified Version of the Patients’ Perceived Involvement in Care Scale.” Journal of Pain and Symptom Management 32 (1): 71–81. doi:10.1016/j.jpainsymman.2006.01.007.
Tarn, Derjung M., Henry N. Young, and Benjamin M. Craig. 2012. “Development of the Patient Approach and Views toward Healthcare Communication (PAV-COM) Measure among Older Adults.” BMC Health Services Research 12: 289. doi:10.1186/1472-6963-12-289.
To identify our shortlist of P3C measures for care planning we first scoped for existing measures, followed by a pragmatic shortlisting process – see the about page for more information. We then mapped the items on the shortlisted questionnaires to domains from the national voices “I” statements. Below, we discuss the questionnaires that had good coverage of care planning.
Overall, this domain was had poor coverage within the shortlisted collection of P3C PRM measures. Of note, there was only one measure that covered all the sub-categories of this domain - the patient-centred coordinated care experience questionnaire (P3CEQ).
Our PenCLAHRC framework of P3C is a nuanced construct that breaks down each of the “I” statement domains into sub-categories. These sub-categories are the real “nuts & bolts” of how these elements of P3C are operationalised in real-world settings. For Care Planning, this is divided into sub-categories of (a) Care Plans, (b) Case Manager, (c) Single Point of Contact and (d) Care Coordination. Examples are given below of the few measures that did tap these individual domain sub-categories.
Only four shortlisted measures explicitly referenced care plans:
- P3CEQ – 4 items
- PAIEC - 3 items
- PACIC – 2 items
- CTM – 1 item
The above measures are all discussed further in the ‘broad measures of P3C’ tab above.
Whilst a care plan was rarely mentioned, the underlying concepts were captured in questions of other measures, albeit in a more generalised manner. For example: “Did you discuss what was most important for YOU in managing your own health and wellbeing?” In our mapping process, these items were coded as a ‘generic care planning’ (i.e. without specific mention of care plans). These measures were:
- IC-PREM-BED & IC-PREM-HOME for older people in community services
- PPRQ - Patient Participation in Rehabilitation Questionnaire
Recently, a pair of PREMs have been designed specifically to evaluate the delivery of person-centred care for older people in intermediate care services – one is designed for home services, the second (IC-PREM-Bed) is designed for bed-based services. They have been developed and validated in the UK context (Teale and Young 2015) as part of the National Audit of Intermediate Care (NAIC). The tools were co-designed with a Delphi panel of experts and patients, with questions being modified to correspond with National Voices “I” statements. The IC-PREMs has been utilised across over 250 IC services audited by the NAIC. Although return rates were low (28% for the bed-based; 13% for the home IC-PREM), the bed-based rates are not dissimilar to other national surveys. Whilst the PREMs are designed to be used across a range of IC services for the purpose of local service improvement, the response scores from these measures can be combined so that an overall service experience score can be generated, suggesting that these measures could be used to facilitate within- and between-service comparisons in the future (Teale and Young 2015).
One significant finding from the implementation of these measures was that while overall care experience was excellent, patients’ experiences of involvement with the decision processes (treatment decisions, discharge planning and goal setting) was more limited. The developers of the measures highlight the relevancy of this finding by highlighting that “patient participation in care planning is increasingly recognised as a key contributor to patient safety and high-quality care” (National Advisory Group on the Safety of Patients in England 2013) and suggest that such findings convey the utility that these measures could have in terms of informing service improvement decisions (Teale and Young 2015).
Patient participation in rehabilitation Questionnaire (PPRQ)
The Patient Participation in Rehabilitation Questionnaire (PPRQ) was developed in Sweden to measures patients’ experiences of participation in care and rehabilitation. Its development co-involved patients. This measure provided had a focused, 4 question section on family involvement. However, further studies with larger samples are required to confirm the scale structure in addition to the sensitivity and responsiveness of the questionnaire (Lindberg et al. 2013).
Only four measures had a single question that discussed case managers:
- P3CEQ
- Components of primary care of Index (CPCI)
- Quality of end of life care (QEOLC - 10)
- Oncology Patients' Perceptions of the Quality of Nursing Care Scale (OPPQNCS)
A brief descriptions of these measure is provided below.
A description of this measure can be found within the “Broad measures of P3C” tab above.
The Components of Primary Care Index (CPCI)
A 20 item tool developed in the USA to assess patient satisfaction with four aspects of primary care delivery (interpersonal communication, the doctor’s accumulated knowledge of the individual patient, patient’s preference) to see their regular doctor and care coordination.
Quality of end of life care (QEOLC - 10)
A 10 item scale to assess and improve the delivery of end-of-life care for patients. Developed in the USA, it focuses on the overarching domain of information and communication. It also taps carer involvement, shared decision making and care planning.
Oncology Patients' Perceptions of the Quality of Nursing Care Scale (OPPQNCS)
The Oncology Patients’ Perceptions of the Quality of Nursing Care Scale (OPPQNCS) was developed from a qualitative study that generated a middle range theory advocating the measurement of the quality of cancer nursing care from the patients’ perspective. It comprises of four subscales: responsiveness, individualization, coordination, and proficiency. Patients are asked to rank the frequency of nursing activities that they experienced.
This sub-category was also referenced very rarely. Measures that referred to it in at least one item include:
- P3CEQ (1 item)
- Oncology Patients’ perceptions of the quality of nursing care scale (OPPQNCS) (3 items)
- Patient perception of continuity instrument (PPCI) (2) items
- OX-PIE (2 items)
Descriptions of these measures are provided below:
P3CEQ – A description of this measure can be found within the “Broad measures of P3C” tab above.
OPPQNCS – described above
Patient perception of continuity instrument (PC)
The PC is a generic measure of management continuity for patients who regularly see more than one clinician, and who are at risk of discontinuity and fragmented care. The measure was developed in Canada with extensive patient involvement. Initial publication revealed it to be a reliable measure of continuity and coordination of care across the entire system (Haggerty et al. 2012).
A UK based 11-item measure for assessing inpatient care for patients with Limiting Long Term conditions (LLLTCs). Items tap areas of the following domains: My goals/ outcomes, care Planning, transitions, decision making and information and communication. It has been used to that Patients with LLTCs were more critical of their inpatient care than those with no LLTCs (Hewitson et al. 2014).
Generic notions of care coordination received more attention in P3C measures than the more specific aspects of the earlier sub-categories (e.g. care plans; case managers; single points of contact). The mapping process identified 3 measures that appeared to most strongly address general aspects of care coordination:
A very brief (4 item) and generic patient-reported measure of integration in health care delivery that can be completed in one minute.It focuses on four distinct domains of integration: information sharing, consistent advice, mutual respect and role clarity. Candidate items were refined and pre-tested via cognitive interviews with end users. Initial validation demonstrated the perceived relevancy and interpretability of the measures items (Elwyn et al. 2015).
Relational and Management Survey in patients with multiple long term conditions
A 16 item measure developed in the UK and co-developed with patients to quantify problems of relational and management continuity of care in patients with multiple long-term conditions. It has been used to demonstrate that people with many long-term conditions are at increased risk of inadequate management continuity with potential negative impacts on their care (Gulliford, Cowie, and Morgan 2011).
The Components of Primary Care Index (CPCI)– see earlier section.
Elwyn, Glyn, Rachel Thompson, Roshen John, and Stuart W. Grande. 2015. “Developing IntegRATE: A Fast and Frugal Patient-Reported Measure of Integration in Health Care Delivery.” International Journal of Integrated Care 15 (March): e008.
Gulliford, Martin, Luke Cowie, and Myfanwy Morgan. 2011. “Relational and Management Continuity Survey in Patients with Multiple Long-Term Conditions.” Journal of Health Services Research & Policy 16 (2): 67–74. doi:10.1258/jhsrp.2010.010015.
Haggerty, Jeannie L., Danièle Roberge, George K. Freeman, Christine Beaulieu, and Mylaine Bréton. 2012. “Validation of a Generic Measure of Continuity of Care: When Patients Encounter Several Clinicians.” Annals of Family Medicine 10 (5): 443–51. doi:10.1370/afm.1378.
Hewitson, Paul, Alex Skew, Chris Graham, Crispin Jenkinson, and Angela Coulter. 2014. “People with Limiting Long-Term Conditions Report Poorer Experiences and More Problems with Hospital Care.” BMC Health Services Research 14 (1): 33. doi:10.1186/1472-6963-14-33.
Lindberg, J, M Kreuter, L-O Person, and C Taft. 2013. “Patient Participation in Rehabilitation Questionnaire (PPRQ)—development and Psychometric Evaluation.” Spinal Cord 51 (11): 838–42. doi:10.1038/sc.2013.98.
National Advisory Group on the Safety of Patients in England. 2013. “Improving the Safety of Patients in England.”
Teale, E. A., and J. B. Young. 2015. “A Patient Reported Experience Measure (PREM) for Use by Older People in Community Services.” Age and Ageing 44 (4): 667–72. doi:10.1093/ageing/afv014.
To identify our shortlist of P3C measures for Information and Communication we first scoped for existing measures, followed by a pragmatic shortlisting process – see the about page for more information. We then mapped the items on the shortlisted questionnaires to domains from the national voices “I” statements. Below, we discuss the questionnaires that had good coverage of the domain “Information and Communication”
Measures that captures this domain most extensively:
Relational and Management Survey in patients with multiple long term conditions
A 16 item measure developed in the UK and co-developed with patients to quantify problems of relational and management continuity of care in patients with multiple long-term conditions. It has been used to demonstrate that people with many long-term conditions are at increased risk of inadequate management continuity with potential negative impacts on their care (Gulliford, Cowie, and Morgan 2011).
Components of Primary Care Index/Instrument (CPCI)
A 20 item tool developed in the USA to assess patient satisfaction with four aspects of primary care delivery (interpersonal communication, the doctor’s accumulated knowledge of the individual patient, patient’s preference to see their regular doctor and care coordination) (Flocke 1997).
Patient perception of continuity instrument (PC)
The PC is a generic measure of management continuity for patients who regularly see more than one clinician, and who are at risk of discontinuity and fragmented care. The measure was developed in Canada with extensive patient involvement. The initial publication revealed it to be a reliable measure of continuity and coordination of care across the entire system (Haggerty et al. 2012).
Our PenCLAHRC framework of P3C is a nuanced construct that breaks down each of the “I” statement domains into sub-categories. These sub-categories are the real “nuts & bolts” of how these elements of P3C are operationalised in real-world settings. For “Information and Communication”, this is divided into sub-categories of
(a) Consistency of contact
(b) PCCC behaviour & communication skills
(c) Information gathering
(d) Knowledge of patient
Below, we briefly discuss those measures that best mapped to these sub-domains of PCCC:
Only one measure had a number of items that mapped onto to this sub-category were:
Components of primary care of Index – see above for brief description
Measures that mapped well onto to this sub-category were:
Components of primary care of Index – see above for brief description
Jefferson Scale of Patient Perceptions of Physician Empathy (JSSSPE)
A brief scale (5 item) for assessing patient’s perceptions of their physician’s empathic engagement. Originates in the USA and concentrates on the domain of therapeutic relationships (PCCC behaviours and communication skills) (Kane et al. 2007).
Four habits patient questionnaire (4HPQ)
Designed as part of a large generic clinical communication program, the patient-reported scale has been shown to correlate with observer-based evaluations of communication. It has evidence for validity and reliability (Krupat et al. 2006).
Communication assessment tool (CAT)
The Communication Assessment Tool (CAT) is a 15 item measure, developed in the US, which can be used by patients to assess the interpersonal and communication skills of physicians. It was co-created with patients, but has not yet been used and validated in a UK context (Makoul, Krupat, and Chang 2007).
Measures that had a number of items that mapped onto to this sub-category were:
Care transitions measure (CTM 15) – A good generic measure of P3C, please see the “Broad Measures of P3C” tab above for more information.
Consultation care measure (CCM)
The CCM is a well-used tool developed in the UK to measures patients’ perceptions of patient-centred care during the last visit with a GP. The instrument has 5 subscales: communication and partnership, personal relationship, health promotion, positive and clear approach to the problem, and interest in effect on life. It has been used to establish that patient-centred approaches are associated with patient satisfaction, and enablement, and that this may reduce symptom burden and rates of referral (Little et al. 2001). It has also been highlighted in a systematic review of tools for measuring patient perceptions of patient-centred care (Hudon et al. 2011).
This measure uses 19 items to capture information from the patient about how their visit with a doctor unfolded. It focuses on both the process aspects of the visit (e.g. patient greeting, listening, and understanding) as well as the content of the visit (e.g. explanations, treatment options, next steps). There is a patient and doctor version and because the item number is relatively low, it can be completed at the end of the visit without causing too much burden to the patient (Campbell et al. 2007).
The original development paper (Campbell et al. 2007) stated that a principle components analysis indicated that 2 factors, process and content, accounted for 52% and 7% of the doctor variance and 60% and 6% of the patient variance, respectively. The linear regression showed that only gender accounted for any of the variance in ratings.
A more recent study (Stoddard 2014) reported that the MPI demonstrated construct validity when it was used with practicing physicians; however, its validity was found to be sensitive to both the medical context (e.g. inpatient setting) and social context (e.g. adults, English speaking patients) in which it is used. Acceptability reliability was only established when a large number of responses were available. Consequently, an independent validation and a reliability analysis would be needed if the MPI was used in different settings. It has been advised that the MPI should not be used for instruction, or educational assessment, as there is no evidence concerning its ability to perform in this way (Stoddard 2014).
Measures that had a number of items that mapped onto to this sub-category were the:
Relational and management continuity survey in patients with multiple long term conditions
Components of primary care Index
Patient perceptions of continuity instrument
Descriptions for the first two measures are provided above; description of the third instrument can be found in the “P3C measures for Care Planning” above.
Campbell, Craig, Jocelyn Lockyer, Toni Laidlaw, and Heather Macleod. 2007. “Assessment of a Matched-Pair Instrument to Examine Doctor-Patient Communication Skills in Practising Doctors.” Medical Education 41 (2): 123–29. doi:10.1111/j.1365-2929.2006.02657.x.
Flocke, S. A. 1997. “Measuring Attributes of Primary Care: Development of a New Instrument.” The Journal of Family Practice 45 (1): 64–74.
Gulliford, Martin, Luke Cowie, and Myfanwy Morgan. 2011. “Relational and Management Continuity Survey in Patients with Multiple Long-Term Conditions.” Journal of Health Services Research & Policy 16 (2): 67–74. doi:10.1258/jhsrp.2010.010015.
Haggerty, Jeannie L., Danièle Roberge, George K. Freeman, Christine Beaulieu, and Mylaine Bréton. 2012. “Validation of a Generic Measure of Continuity of Care: When Patients Encounter Several Clinicians.” Annals of Family Medicine 10 (5): 443–51. doi:10.1370/afm.1378.
Hudon, Catherine, Martin Fortin, Jeannie L. Haggerty, Mireille Lambert, and Marie-Eve Poitras. 2011. “Measuring Patients’ Perceptions of Patient-Centered Care: A Systematic Review of Tools for Family Medicine.” Annals of Family Medicine 9 (2): 155–64. doi:10.1370/afm.1226.
Kane, Gregory C., Joanne L. Gotto, Salvatore Mangione, Susan West, and Mohammadreza Hojat. 2007. “Jefferson Scale of Patient’s Perceptions of Physician Empathy: Preliminary Psychometric Data.” Croatian Medical Journal 48 (1): 81–86.
Krupat, Edward, Richard Frankel, Terry Stein, and Julie Irish. 2006. “The Four Habits Coding Scheme: Validation of an Instrument to Assess Clinicians’ Communication Behavior.” Patient Education and Counseling 62 (1): 38–45. doi:10.1016/j.pec.2005.04.015.
Little, P., H. Everitt, I. Williamson, G. Warner, M. Moore, C. Gould, K. Ferrier, and S. Payne. 2001. “Observational Study of Effect of Patient Centredness and Positive Approach on Outcomes of General Practice Consultations.” BMJ 323 (7318): 908–11. doi:10.1136/bmj.323.7318.908.
Makoul, Gregory, Edward Krupat, and Chih-Hung Chang. 2007. “Measuring Patient Views of Physician Communication Skills: Development and Testing of the Communication Assessment Tool.” Patient Education and Counseling 67 (3): 333–42. doi:10.1016/j.pec.2007.05.005.
Stoddard, Hugh. 2014. “Critical Synthesis Package: Matched Pair Instrument (MPI) in Doctor-Patient Communication Skills.” MedEdPORTAL Publications. doi:10.15766/mep_2374-8265.9847.
To identify our shortlist of P3C measures for the domain containing “transitions and continuity”, we first scoped for existing measures, followed by a pragmatic shortlisting process – see the about page for more information. We then mapped the items on the shortlisted questionnaires to domains from the national voices “I” statements.
We discovered that transition (i.e. continuity of care) is one of the most poorly represented domain of P3C measures. There were only three measures that contained three or more questions related to this domain: the Care Transitions Measure (CTM-15), The Assessment of Care for Chronic Conditions (PACIC) and the Patient Assessment of Integrated Elderly Care (PAIEC).
As the PAIEC (for elderly people) is a revised version of the PACIC (for chronic conditions), domain coverage of these two measures is very similar. A short description of both these measures is provided in the ‘Broad P3C measures’ section (on the tabs above). Neither of these tools can be described as offering an extensive measurement of continuity of care. Instead, they offer a broad coverage of person-centred care, which includes continuity of care.
In contrast, the CTM – discussed in more detail below - was the only measure we identified (after shortlisting criteria were applied) that was solely designed for measuring transitions. However, the measure is very targeted towards the experiences of care during the transition from hospital to home, and does not cover important aspects of P3C such as continuity and consistency of coordination.
If continuity of care was the intended focus for a PRM then it would be worth considering whether a new measure should be developed for this intended purpose.
Care Transition Measure (CTM-15)
The most widely used measure of care transition quality is the Care Transitions Measure (CTM-15) (Coleman et al. 2002). There is also a 3-item version available. It is comprised of 15 items in four “transition domains”, derived from patient focus groups, which are describe as (1) Information Transfer, (2) Patient and Caregiver Preparation, (3) Support for Self-Management, and (4) Empowerment to Assert Preferences. Translated into “I” statement, it retains good coverage of a variety of aspects of person-centredness, with the exception of single point of contact/key worker and therapeutic relationship.
However, poor psychometric properties have been reported. An independent evaluation revealed that he CTM-15 had good internal consistency (Cronbach's α=0.95) but demonstrated acquiescence bias (8.7% participants responded “Strongly agree” and 19% responded “Agree” to all items) and limited score variability (Anatchkova et al. 2014).
Anatchkova, M. D., C. M. Barysauskas, R. L. Kinney, C. I. Kiefe, A. S. Ash, L. Lombardini, and J. J. Allison. 2014. “Psychometric Evaluation of the Care Transition Measure in TRACE-CORE: Do We Need a Better Measure?” Journal of the American Heart Association 3 (3): e001053–e001053. doi:10.1161/JAHA.114.001053.
Coleman, Eric A., Jodi D. Smith, Janet C. Frank, Theresa B. Eilertsen, Jill N. Thiare, and Andrew M. Kramer. 2002. “Development and Testing of a Measure Designed to Assess the Quality of Care Transitions.” International Journal of Integrated Care 2: e02.
To identify our shortlist of P3C measures for shared decision making (SDM) we first scoped for existing measures, followed by a pragmatic shortlisting process – see the about page for more information. We then mapped the items on the shortlisted questionnaires to domains from the national voices “I” statements. Below, we discuss the questionnaires that had good coverage of SDM.
The questionnaires varied substantially in how they sought to measure the patients’ perspective of SDM. Whilst some measures sought to capture this information with a few focused questions, others were more extensive. Furthermore, while some measures focused on the patient’s ability and confidence to make decisions, others focused on how the doctor’s actions may have facilitated shared decision making.
One markedly different approach was undertaken by the brief 3 item measure CollaboRATE, as it did not use the word decision within any of its 3 items. This design choice was taken due to the concern that the inclusion of such words would negatively impact on the interpretability of the items (and thus, the validity of the measures) for some patients. An explanation for this viewpoint is provided within the CollaboRATE section below.
Another distinctive measurement tool – for a very different reason - is the dyadic OPTION measurement tool. The items of this tool have been designed to be non-person specific, so that both the patient and the health professional can answer exactly the same questions. This allows both viewpoints to inform the assessment of SDM.
Due to such variations in design, any selection of a decision making measure will vary depending on the context in which it is used. One key issue is the trade-off between the low response burden of short tools versus the detail of longer tools.
CollaboRATE (a brief measure of shared-decision making)
CollaboRATE was designed to be a rapid and efficient measure of patients’ experiences of shared decision making. It contains three questions that patients (or parents/carers) complete following a health care encounter. It is intended to be a generic measure that could be used in both research and routine heath care settings (Barr, Thompson, Walsh, Grande, Ozanne & Elwyn, 2014).
As mentioned previously, none of the items within CollaboRATE explicitly mention the word “decision” or related words such as “preference”. The developers opted to exclude these words because they were concerned about negative impacts on the patients’ ability to interpret the items. This is because a health care encounter will often involve more than just one single decision (Hauer, Fernandez, Teherani, Boscardin & Saba, 2011; Weiss & Peters, 2011) and patients may not always realize that a decision has been made (Entwistle, Skea & O'Donnell, 2001; Entwistle, Watt Gilhooly, Bugge, Haites & Walker 2004). Furthermore, patients may not want to take any role in the decision-making process.
The developers state that their unique approach to how they worded their questions renders ColloboRATE more understandable to patients (Entwistle, Skea & O'Donnel, 2001; Entwistle et al, 2006), and distinguishes CollobRATE from the other short whole-encounter measures of the SDM process that currently exist [Martin, DiMatteo & Lepper 2001; Degner, et al, 1997).
While the word “decision” is not included in the items, the questions are based on core aspects of the principles of shared decision making (Cohen, Colliver, Marcy, Fried & Swartz, 1996; DeSalvo et al., 2009; Schnabl, Hassard & Kopelow, 1991; Winterbottom, Bekker, Conner & Mooney, 2012) and on a detailed analysis of existing measurement challenges (Coulter, Edwards, Elwyn & Thomson, 2011). As a result, the 3 items assess the extent to which each of 3 core shared decision-making tasks occurred within a health-care encounter: (1) explanation of the health issue, (2) elicitation of patient preferences, and (3) integration of patient preferences (Elwyn, Barr, Grande, Thompson, Walsh, Ozanne 2013).
Of note is that our mapping of CollaboRATE onto the “I” statements P3C domains resulted in this measure not appearing to be one that was particularly focused on SDM. Whilst one item was explicitly applicable to the decision-making domain, the other two items focus on preliminary tasks necessary for SDM i.e. helping the patient to understand their health needs and listening to what matter most to the patient. The questionnaire does not give any indication that these tasks are to be related to an experience of shared-decision making. Consequently, the connection to SDM was not coded by the objective criteria of our mapping process. Instead, the items corresponded to domains such as communication, goal setting and empowerment. The impact of such tensions (e.g. between the stated goals versus the actual language) should be evaluated depending on the context in which the questionnaire is used.
Initial evaluations of CollaboRATE revealed that the instrument has a very high level of acceptability: less than 1% of participants missed any of the items. Discriminative validity, concurrent validity, intra-rater reliability, and sensitivity to change was also demonstrated. Divergent validity was not established. The measure was found to be particularly effective at discriminating between the absence and presence of any level of shared-decision making. Scores were shown to be consistent when retested over a 1- to 2-week period (Barr et al, 2014).
However, the instrument was evaluated using simulated medical encounters (Barr et al, 2014). Potential shortcomings of this method were acknowledged. For example, the simulated encounters only dealt with 1 health issue, which is not an accurate reflection of the complexity that is often present in primary care (Barr et al, 2014).
There are many practical advantages of using this tool: it allows for rapid and simple analysis, it doesn’t place a high burden on the respondent, it is cost-effective, has strong face-validity, is easy to implement (Bowling, 2005; Littman, White, Satia, Bowen & Kristal, 2006) is generic, and potentially allows for comparisons across condition types (Barr et al, 2014). If large scale-use and rapid feedback is desired, then this measure is worth consideration.
SURE - a brief measure of decision making.
Decisional conflict is a term used to describe a person’s perception of uncertainty when the choice involves risk, loss, regret, or a challenge to personal life values (Carpenito, 2000). The decisional conflict scale (DCS) was developed to limit the impact of negative repercussions (caused by unresolved decisional conflict) by using the tool to identify decisional conflict in patients and then providing them with appropriate support (O’Connor, Légaré & Stacey, 2003). Despite the clinical value of the DCS, the implementation of the tool has been discouraged by the time it takes to administer (Légaré et al, 2007). SURE was developed as a briefer alternative to DCS that would allow health professionals to rapidly identify patients with significant decisional conflict (Légaré, et al, 2010).
The selection process for the four items within the measure was based on the core concepts of the Ottawa Decision Support Framework, which are reported as having relevancy at all stages of the decision making process: feeling uncertain, feeling informed, feeling clear about values and feeling supported in decision-making (O’Connor, et al, 1998). The items were developed in French and English concurrently and were all positively framed. The 4 items were screened by field tested experts and graduate students taking clinical courses in decision support
Development of the measure suggested that SURE - as a screening test for decisional conflict - has appropriate psychometric qualities and would be suitable for use within primary care (Légaré, et al, 2010). However, there was no mention of co-involvement of patients in the design of the questionnaire. Furthermore, the refinement of one item (support) should be considered (due to issues surrounding unidimensionality) and further evaluations across broader group of patients is required (Légaré, et al, 2010).
Dyadic OPTION (observing patient involvement in decision making).
Historically, measures of SDM have only been assessed from the perspective of either the patient or the health practitioner (Melbourne, Sinclair, Durand, Le´gare & Elwyn, 2010). Instead, the developers of the dyadic OPTION measure argued that SDM is shaped by the interaction of both members of the interaction, and that patients’ and health practitioners’ perspectives can sometimes be contradictory (Braddock, Fihn, Levinson, Jonsen & Pearlman, 1997). Measuring both viewpoints can help understand “the interdependence in dyadic interaction affects the outcome of the encounter” (Braddock, Edwards, Hasenberg, Laidley & Levinson, 1999; Elwyn et al, 2005; Gibson, Jenkings, Wilson & Purves 2006).
The dyadic version of the existing OPTION measure was developed from an existing decision making tool. In this way, it could benefit from the rigorous testing that had already been applied to the original measure, including psychometric data to support its uni-dimensional nature (Cook & Kenny, 2005; LeBlanc, Kenny, O’Connor & Legare, 2009). Initial publication concluded that the finished dyadic OPTION scale is acceptable and comprehensible by both health practitioners and public respondents (Melbourne et al, 2010). However, the authors did state that further validation of the dyadic OPTION is required prior its use in research settings (Melbourne et al, 2010).
SDM-Q-9 - the shared decision making questionnaire – 9 items
The SDM-Q-9 is an adapted version of the existing Shared Decision Making Questionnaire (SDM-Q) that includes new items and a revised response format. This was necessary as the original measure had several items that showed non-uniform characteristics (Kriston, Scholl, Holzel & Harter, 2009).
While many other SDM measures are framed solely on the patient’s ability to make decisions, the items within the SDM-Q-9 are focused on whether the doctor’s actions facilitated SDM and capture the extent to which SDM occurred. Consequently, the SDM-Q-9 can be used in studies investigating the effectiveness of SDM interventions and as a quality indicator in health services assessments.
Findings showed that this measure is reliable, has face validity and is a well-accepted measure (Kriston et al, 2010). A comparison of the SDM-Q-9 and the original OPTION scale (scored by expert observers), strengthened the psychometric standing of the measure. Whilst the OPTION scale had limited internal consistency and the inter-rater reliability was low, the SDM-Q-9 demonstrated good internal consistency (Scholl, Kriston, Dirmaier & Harter, 2015). However, generalizability of these findings is limited as the measure has only been tested with elderly Germans. Whilst this measure looks promising, further validation is necessary.
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Individualised PROs (Browne 2014) such as the Person Generated Index (Ruta et al. 1994) allow patients to modify the content or scoring system, prioritising the symptoms to address (Valderas and Alonso 2008). Such patient empowerment is particularly salient to complex scenarios such as mLTCs (Jolles, Buchbinder, and Beaton 2005). However, there are disadvantages of such measures that include increased burden (for researcher, clinician, administrator and patient), methodological/analytical complexities (Dijkers 2003; Jolles, Buchbinder, and Beaton 2005) and problems with aggregation. One review stated that “their most important role may be in … a consultation process” (Browne 2014).
COPM - Canadian Occupational Performance Measure
Overall, the Canadian Occupational Performance Measure (COPM) (Law et al. 1990) is deemed reliable and clinically useful for occupational therapist practitioners. It offers a broad focus on occupational performance across a range of areas including self-care, leisure and productivity, and takes into account personal life circumstances. It has been used with a variety of client groups, although there is some evidence that it is not suitable for those with low empowerment/ self-management skills.
The Goal Attainment Scaling (GAS) (Kiresuk and Sherman 1968) was originally used in mental health settings, but has also been undertaken in elderly care settings as a goal setting facilitator for chronic pain and in cognitive and amputee rehabilitation. A considerable literature base suggests its usefulness within a person centred decision making process.
Although not specific to goal setting, Talking Mats have been used effectively with those with cognitive impairment to facilitate participation in discussions that they may have difficulty in engaging in otherwise (Murphy et al. 2005)
PGI - Patient Generated Index / Modified Patient Generated Index
In addition, we have identified the Patient Generated Index / Modified Patient Generated Index (PGI)(Ruta et al. 1994). Whilst this is primarily a quality of life measure, it does allow the individual to select personally relevant areas for improvement through the application of a point scale. Various adaptations have been made to suit a variety of patient groups. It has a well-developed conceptual model, and the modified version is particularly good at detecting change.
MYMOP - The Measure Yourself Medical Outcome Profile
The Measure Yourself Medical Outcome Profile (MYMOP) (Paterson, 1996) is an individualised outcome questionnaire. Whilst problem specific, it also includes items on general wellbeing and is relevant across physical, emotional and social symptoms. It is a brief measure and is simple to administer.
The Human Five is a recently developed tool that allows patients to set personal goals on five key areas of health and wellbeing: mind, body, nutrition, movement and their world.
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With regard to suitable QoL instruments for diabetes, it has been established that while generic instruments such as the SF-36 and EQ-5D are valid and reliable, they are strongly affected by non-diabetic comorbidity (Woodcock et al. 2001). Such findings should encourage the complementary use of a diabetes-specific measures (Woodcock et al. 2001). Generic measures such as the SF-36 and EQ-5D have been recommended as suitable in diabetes alongside condition-specific measures, with the SF-36 being most highly validated as a generic QoL measure for diabetes (Oxford PROMS group 2009).
Reviews of diabetes-specific measures highlight two diabetes HrQoL measures that are short, well-translated, well used in the UK context and have been independently assessed as having well-validated psychometric properties, with both demonstrating reliability as well as internal and external construct validity (El Achhab et al. 2008; Garratt, Schmidt, and Fitzpatrick 2002; Oxford PROMS group 2009):
ADDQoL - The Audit of Diabetes Dependent QoL
Three independent reviews have established good evidence for reliability, in addition to internal and external construct validity for this measure (El Achhab et al. 2008; Garratt, Schmidt, and Fitzpatrick 2002; Oxford PROMS group 2009). Furthermore, it is the most widely translated measure (Bradley et al. 1999; Garratt, Schmidt, and Fitzpatrick 2002). There are 14 item and 19 item versions available.
DHP1/18 – Diabetes Health Profile
The DHP has 18 item and 32 item versions, over 14 translations, is well used in the UK context and has been independently assessed as having well-validated psychometric properties (El Achhab et al. 2008; Garratt, Schmidt, and Fitzpatrick 2002; Oxford PROMS group 2009).
Self-management is often highlighted as a key component of person-centred approaches in diabetes care. As part of a P3C approach, diabetes patients are increasingly self-managing their illness with the support and assistance of health care professionals. This interactive process partly depends on patients’ reports on their self-care. As such, the application of self-report measures of diabetes self-care has continued to grow, in particular, within the last decade.
A recent systematic review of instruments for diabetes self-management identified 21 original instruments, although most had not been rigorously evaluated (Lu et al. 2016). The most widely used and validated instruments were the SDSCA, DCP, MARS, MMAS and SCI-R. Of these, the DCP has been used in a UK context (Baksi et al. 2008), and the SCI-R – a more modern measure – has been recently validated in the UK (Khagram et al. 2013). The SDSCA is the most widely used and validated tool, although we could not identify examples of its use in a UK context.
The self-report instruments used to measure diabetes self-care can be divided into two categories: one relies on patients’ reports on frequency of a specific self-care behaviour over a certain time period (e.g., SDSCA and DSCAQ) and another relies on patients’ reports on their perceptions of their self-care behaviours (e.g., SCI). The latter may take into account differences in individual prescriptions, but subjectivity can be relatively stronger. Which method is a more accurate, practical, and an easy-to-use measurement is controversial.
Furthermore, diabetes self-care is complex and multidimensional, encompassing concepts including diet, physical activity, medications, self-monitoring of blood glucose, and foot care. There is no universally accepted definition of the most appropriate domains, leading to challenges in assessing diabetes self-care. It has been recently argued that to more intense efforts should be devoted towards establishing high-quality diabetes self-care instruments(Lu et al. 2016). Of the most widely used and best-validated measures, the following two have been validated in a UK context:
The Diabetes Care Profile was developed in the USA in the 1990s and is a one of the most frequently used measures for self-management in diabetes. It is a brief, standardized 8-item instrument for assessing social and psychological factors related to diabetes and its treatment.
SCI-R - Self-Care Inventory Revised
Measures of self-care need to be updated regularly to maintain relevance to modern treatments and technologies. The Self-Care Inventory Revised is a scale that has been modified to reflect current diabetes practice. Unlike measures that assess the frequency of certain behaviours, the Self-Care Inventory Revised (SCI-R) does not presume an “ideal” regimen or that all individuals have the same regimen. Rather, the SCI-R evaluates individuals’ perceptions of well engaged patients are with their individualised treatment recommendations. It has been reported to be a psychometrically sound measure of engagement with recommended diabetes self-care (Weinger et al. 2005). Recently, the SCI-R was shown to be a valid and reliable measure of self-care in people with type 2 diabetes in the UK. However, ceiling effects raise concerns about its potential for responsiveness (Khagram et al. 2013).
The DIAB-Q - The Diabetes Intention, Attitude, and Behaviour Questionnaire
The DIAB-Q is a newly developed measure, which distinguishes itself from exiting measures by measuring the intention to engage in self-care behaviours under a Theory of Planned Behaviour (TPB) framework. Initial publications have shown it to be psychometrically sound (Traina et al. 2016). The authors argue that identification of gaps in patients’ self-care attitudes and practices could allow health care providers to implement targeted and person-centred approaches that incorporate individual beliefs and preferences. This could support individualized diabetes self-management education, and support plans to help patients meet their medical needs, and achieve their treatment goals.
DES - Diabetes Empowerment Scale
The Diabetes Empowerment Scale is a well-used tool to measure psychosocial adjustment to diabetes and psychosocial self-efficacy (Anderson et al. 2000). It has been used in the UK (Frost et al. 2013), has numerous translations and has reasonable psychometric properties (Barr et al. 2015).
Anderson, R. M., M. M. Funnell, J. T. Fitzgerald, and D. G. Marrero. 2000. “The Diabetes Empowerment Scale: A Measure of Psychosocial Self-Efficacy.” Diabetes Care 23 (6): 739–43. doi:10.2337/diacare.23.6.739.
Baksi, A. K., M. Al-Mrayat, D. Hogan, E. Whittingstall, P. Wilson, and J. Wex. 2008. “Peer Advisers Compared with Specialist Health Professionals in Delivering a Training Programme on Self-Management to People with Diabetes: A Randomized Controlled Trial.” Diabetic Medicine: A Journal of the British Diabetic Association 25 (9): 1076–82. doi:10.1111/j.1464-5491.2008.02542.x.
Barr, Paul J., Isabelle Scholl, Paulina Bravo, Marjan J. Faber, Glyn Elwyn, and Marion McAllister. 2015. “Assessment of Patient Empowerment--a Systematic Review of Measures.” PloS One 10 (5): e0126553. doi:10.1371/journal.pone.0126553.
Bradley, C., C. Todd, T. Gorton, E. Symonds, A. Martin, and R. Plowright. 1999. “The Development of an Individualized Questionnaire Measure of Perceived Impact of Diabetes on Quality of Life: The ADDQoL.” Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation 8 (1–2): 79–91.
El Achhab, Youness, Chakib Nejjari, Mohamed Chikri, and Badiaa Lyoussi. 2008. “Disease-Specific Health-Related Quality of Life Instruments among Adults Diabetic: A Systematic Review.” Diabetes Research and Clinical Practice 80 (2): 171–84. doi:10.1016/j.diabres.2007.12.020.
Frost, Julia, Rob Anderson, Catherine Argyle, Mark Daly, Faith Harris-Golesworthy, Jim Harris, Andy Gibson, et al. 2013. “A Pilot Randomised Controlled Trial of a Preconsultation Web-Based Intervention to Improve the Care Quality and Clinical Outcomes of Diabetes Outpatients (DIAT).” BMJ Open 3 (7). doi:10.1136/bmjopen-2013-003396.
Garratt, A. M., L. Schmidt, and R. Fitzpatrick. 2002. “Patient-Assessed Health Outcome Measures for Diabetes: A Structured Review.” Diabetic Medicine: A Journal of the British Diabetic Association 19 (1): 1–11.
Khagram, Leena, Colin R Martin, Melanie J Davies, and Jane Speight. 2013. “Psychometric Validation of the Self-Care Inventory-Revised (SCI-R) in UK Adults with Type 2 Diabetes Using Data from the AT.LANTUS Follow-on Study.” Health and Quality of Life Outcomes 11 (1): 24. doi:10.1186/1477-7525-11-24.
Lu, Y., J. Xu, W. Zhao, and H.-R. Han. 2016. “Measuring Self-Care in Persons With Type 2 Diabetes: A Systematic Review.” Evaluation & the Health Professions 39 (2): 131–84. doi:10.1177/0163278715588927.
Oxford PROMS group. 2009. “A STRUCTURED REVIEW OF PATIENT-REPORTED OUTCOME MEASURES (PROMs) FOR DIABETES.” Oxford.
Weinger, Katie, Heather A. Butler, Garry W. Welch, and Annette M. La Greca. 2005. “Measuring Diabetes Self-Care: A Psychometric Analysis of the Self-Care Inventory-Revised with Adults.” Diabetes Care 28 (6): 1346–52.
Woodcock, A. J., S. A. Julious, A. L. Kinmonth, M. J. Campbell, and Diabetes Care From Diagnosis Group. 2001. “Problems with the Performance of the SF-36 among People with Type 2 Diabetes in General Practice.” Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation 10 (8): 661–70.
The Oxford PROMs group have reviewed QoL measures for the four most common types of cancer: breast, Prostrate, Lung and Colorectal. For generic measures, across all conditions, the SF-36 and SF-12 were preferred. Use of the EQ-5D is advised for situations where a short preference measure is required (Hamoen et al. 2015; Maratia, Cedillo, and Rejas 2016). These measures have been established as having discriminant validity and may be sufficient for some purposes, although cancer specific instruments are more responsive.
An overview of the evidence within this field suggests that the EORTC QLQ-30 and FACT-G are the most preferred cancer-specific QoL measures. An assessment of QoL measures for specific types of cancer is beyond the scope of this work. However, measures developed by both FACT and EORTC are generally well regarded for this purpose. Such cancer-specific measures have been found to be more responsive than generic measures, such as the WHO-QoL BREF and the SF-36 (Maratia, Cedillo, and Rejas 2016).
A recent review (specifically for prostate cancer survivor follow up) again highlighted the good QoL measures that exist for cancer (Hudson, O’Malley, and Miller 2015). However, the review also stressed that very few measures were related to health promotion and care quality within the prevention, surveillance and care co-ordination components of cancer survivorship. They did highlight some care quality outcomes, the majority of which were very specific tools (e.g. prostate-cancer specific) beyond the scope of this review (Hudson, O’Malley, and Miller 2015).
Patient-reported measures have been used to establish that the overall health profiles of cancer survivors are broadly similar to those with serious LTCs (Elliott et al. 2011; Glaser et al. 2013; Corner et al. 2013) and that those who also report a chronic illness are in poorer health still (Elliott et al. 2011). This has led to the suggestion that such populations should be re-classified as LTCs, with a subsequent emphasis on long term care (Warrington, Absolom, and Velikova 2015; Coulter 2016).
Reviews of tools for assessing long-term cancer survivors highlighted two tools (Muzzatti and Annunziata 2013; Pearce, Sanson-Fisher, and Campbell 2008; Chopra and Kamal 2012) as having the most systematic effort towards validation, one of which (QLACS) has been used in a large national UK study. Details of both of these measures are provided below.
QLACS- Quality of Life in Adult Cancer Survivors
The QLACS is a measure specifically focusing on the quality of life of long-term cancer survivors (Avis, Ip, and Foley 2006). It is based on the multidimensional cancer-related quality of life model, which takes into account both functioning and patient satisfaction with functioning. Cancer survivorship is defined as a period of 5 years or more post-diagnosis. It has extremely broad domain coverage, exploring appearance concerns, financial problems, distress over recurrence, family-related distress, and the benefits of cancer. It has been used in a large UK national follow up study of cancer survivors and therefore its applicability to UK populations has been validated (Elliott et al. 2011).
IOCv2 - Impact of Cancer Scale v2
The IOC was developed specifically to measure unique and multidimensional aspects of long-term cancer survivorship focusing almost exclusively on problems, issues, and changes that long-term survivors ascribe to their cancer experience. The IOC/IOCv2 focuses on the meaning attributed to cancer, health awareness and health worries, body image concerns, cancer interference in social life, personal self-evaluation, life outlook, pro-social behaviours (i.e., altruism, empathy). Whilst is has been highlighted as a measure that has received one of the most systematic efforts towards validation (Muzzatti and Annunziata 2013), we could not identify its use within a UK context.
A further recent review of patient-centred tool for cancer care identified 21 PROMS (Tzelepis et al. 2014). However, most of these measures are beyond the scope of this work, as they have been developed for very specific situations (e.g. specific cancer types). However, the study did highlight that none of the tools covered all domains of person-centred and coordinated care, and that rigorous psychometric testing was often lacking. Nonetheless, some of the tools relevant to this section are described below:
Whilst this compendium does not contain tools for specific cancer types, one tool that we have decided to highlight is the ALGA-Breast Cancer (ALGA-BC) measure. This is a new multidimensional questionnaire that has been designed with the explicit goals of delivering personalised and patient centred-medicine. It assesses the breast cancer patient's physical and mental characteristics in order to provide physicians, prior to the consultation, with a patient's profile that is supposed to facilitate subsequent communication, interaction, and information delivery between the doctor and the patient (Gorini et al. 2015).
MCQ - Medical Care Questionnaire
One tool – the Medical Care Questionnaire (a modified version of the CPCI) (Harley et al. 2009) was developed in a UK context for measuring out-patient experiences of continuity of cancer care and that covers domains of care co-ordination, emotional support and patient respect.
Furthermore, the Modified Perceived Involvement in Care Scale has been tested within a cancer setting, but not in the UK.
The TiOS (Trust in Oncologist Scale) is a tool for studying communication within an oncological setting, which has received positive independent evaluation (Muller et al. 2014).
Avis, Nancy E., Edward Ip, and Kristie Long Foley. 2006. “Evaluation of the Quality of Life in Adult Cancer Survivors (QLACS) Scale for Long-Term Cancer Survivors in a Sample of Breast Cancer Survivors.” Health and Quality of Life Outcomes 4: 92. doi:10.1186/1477-7525-4-92.
Chopra, Ishveen, and Khalid M. Kamal. 2012. “A Systematic Review of Quality of Life Instruments in Long-Term Breast Cancer Survivors.” Health and Quality of Life Outcomes 10: 14. doi:10.1186/1477-7525-10-14.
Corner, J., R. Wagland, A. Glaser, and S. M. Richards. 2013. “Qualitative Analysis of Patients’ Feedback from a PROMs Survey of Cancer Patients in England.” BMJ Open 3 (4): e002316–e002316. doi:10.1136/bmjopen-2012-002316.
Coulter, Angela. 2016. “Caroline Potter Michele Peters Ray Fitzpatrick.” Accessed February 2. http://www.pssru.ac.uk/archive/pdf/5079.pdf.
Elliott, J, A Fallows, L Staetsky, P W F Smith, C L Foster, E J Maher, and J Corner. 2011. “The Health and Well-Being of Cancer Survivors in the UK: Findings from a Population-Based Survey.” British Journal of Cancer 105 (November): S11–20. doi:10.1038/bjc.2011.418.
Glaser, A. W., L. K. Fraser, J. Corner, R. Feltbower, E. J. A. Morris, G. Hartwell, M. Richards, and R. Wagland. 2013. “Patient-Reported Outcomes of Cancer Survivors in England 1-5 Years after Diagnosis: A Cross-Sectional Survey.” BMJ Open 3 (4): e002317–e002317. doi:10.1136/bmjopen-2012-002317.
Gorini, Alessandra, Ketti Mazzocco, Sara Gandini, Elisabetta Munzone, Gordon McVie, and Gabriella Pravettoni. 2015. “Development and Psychometric Testing of a Breast Cancer Patient-Profiling Questionnaire.” Breast Cancer (Dove Medical Press) 7: 133–46. doi:10.2147/BCTT.S80014.
Hamoen, Esther H. J., Maarten De Rooij, J. Alfred Witjes, Jelle O. Barentsz, and Maroeska M. Rovers. 2015. “Measuring Health-Related Quality of Life in Men with Prostate Cancer: A Systematic Review of the Most Used Questionnaires and Their Validity.” Urologic Oncology 33 (2): 69.e19-28. doi:10.1016/j.urolonc.2013.10.005.
Harley, Clare, Jacqui Adams, Laura Booth, Peter Selby, Julia Brown, and Galina Velikova. 2009. “Patient Experiences of Continuity of Cancer Care: Development of a New Medical Care Questionnaire (MCQ) for Oncology Outpatients.” Value in Health: The Journal of the International Society for Pharmacoeconomics and Outcomes Research 12 (8): 1180–86. doi:10.1111/j.1524-4733.2009.00574.x.
Hudson, Shawna, Denalee O’Malley, and Suzanne Miller. 2015. “Achieving Optimal Delivery of Follow-up Care for Prostate Cancer Survivors: Improving Patient Outcomes.” Patient Related Outcome Measures, March, 75. doi:10.2147/PROM.S49588.
Maratia, Stefano, Sergio Cedillo, and Javier Rejas. 2016. “Assessing Health-Related Quality of Life in Patients with Breast Cancer: A Systematic and Standardized Comparison of Available Instruments Using the EMPRO Tool.” Quality of Life Research?: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, April. doi:10.1007/s11136-016-1284-8.
Muller, Evamaria, Jordis M. Zill, Jorg Dirmaier, Martin Harter, and Isabelle Scholl. 2014. “Assessment of Trust in Physician: A Systematic Review of Measures.” PloS One 9 (9): e106844. doi:10.1371/journal.pone.0106844.
Muzzatti, Barbara, and M. Antonietta Annunziata. 2013. “Assessing Quality of Life in Long-Term Cancer Survivors: A Review of Available Tools.” Supportive Care in Cancer: Official Journal of the Multinational Association of Supportive Care in Cancer 21 (11): 3143–52. doi:10.1007/s00520-013-1912-6.
Pearce, Nancy J. M., Rob Sanson-Fisher, and H. Sharon Campbell. 2008. “Measuring Quality of Life in Cancer Survivors: A Methodological Review of Existing Scales.” Psycho-Oncology 17 (7): 629–40. doi:10.1002/pon.1281.
Tzelepis, Flora, Shiho K Rose, Robert W Sanson-Fisher, Tara Clinton-McHarg, Mariko L Carey, and Christine L Paul. 2014. “Are We Missing the Institute of Medicine’s Mark? A Systematic Review of Patient-Reported Outcome Measures Assessing Quality of Patient-Centred Cancer Care.” BMC Cancer 14 (1): 41. doi:10.1186/1471-2407-14-41.
Warrington, Lorraine, Kate Absolom, and Galina Velikova. 2015. “Integrated Care Pathways for Cancer Survivors – a Role for Patient-Reported Outcome Measures and Health Informatics.” Acta Oncologica 54 (5): 600–608. doi:10.3109/0284186X.2014.995778.
The primary focus below has been on identifying measures suitable for use in community mental health settings, and as such the measures listed may not be suitable for inpatient or secure settings, or for those who lack capacity.
Generic quality of life measures, such as the EQ-5D and SF-36, may not be appropriate for use within a mental health context especially for people with severe mental health problems (Brazier et al, 2014), although evidence suggest they are acceptable for depression. There are measures that are either currently being developed or have recently been developed, such as the ICE-CAP-A and ReQoL
The ICE-CAP-A (Al-Janabi et al, 2012) is a capability measure for the general adult population. Unlike the EQ-5D (which focuses on health), the ICA-CAP-A focuses on wellbeing defined in a broader sense and therefore may be a more appropriate measure for mental illness. However, the ICE-CAP-A has been shown to have acceptable content and construct validity, but as a relatively new measure, the psychometric properties are yet to be fully demonstrated. However, it has been developed to be a more appropriate preference measure for mental health than the EQ-5D, but is intended to be used in health economics in a similar way, but focusing more on wellbeing than health.
Recovering Quality of Life (ReQol) is a recently developed tool to assess quality of life for people with different mental health conditions. It is noteworthy as it has been specifically developed to be a generic outcome measure that could be used across the spectrum of mental health problems (severe mental illness as well a more common problems such as depression/anxiety).This measure is not yet available but should be during the second half of 2016 – see www.reqol.org.uk.
The Warwick-Edinburgh Mental Well-being Scale (WEMWBS) is a 14 item scale of mental well-being covering subjective well-being and psychological functioning. It was originally validated for use in the UK adult population, but has since been validated in a number of different languages and English speaking ethnic groups. It is used extensively within IAPT so has a good psychometric base. Alternative such as the CORE-OM is long (34 items) and the CORE-10 is not as widely used as the WEMWBS.
A systematic review and narrative synthesis of recovery has identified five recovery processes. These are: connectiveness, hope and optimism, identity, meaning and purpose, and empowerment, giving the acronym CHIME. The CHIME framework provides a conceptual framework against which to compare recovery measures.
Two systematic reviews of measures of recovery (Burgess et al, 2011, and Shanks et al, 2013) have indicated that there is good evidence for the reliability and validity of the Recovery Assessment Scale (RAS; Corrigan et al, 1999) as a measure of recovery. The more recent review also indicated that the Questionnaire About the Processes of Recovery (QPR; Neil, Kilbride and Pitt, 2009) was the measure that most closely mapped onto the CHIME framework, and captured the five recovery process identified in this framework (connectedness, hope and optimism, identity, meaning and purpose, and empowerment). The RAS is the most widely published measure of recovery, but at 41 items is nearly twice as long as the QPR (22 items), and this may impact on response rate.
Al-Janabi H, Flynn TN, Coast J. 2012. Development of a self-report measure of capability wellbeing for adults: the ICECAP-A. Quality of Life Research 21: 167–176.
Brazier, J., Connell, J., Papaioannou, D., Mukuria, C., Mulhern, B., Peasgood, T., Lloyd-Jones, M. et al. 2014. A systematic review, psychometric analysis and qualitative assessment of generic preference-based measures of health in mental health populations and the estimation of mapping functions from widely used specific measures. Health Technology Assessment (Winchester, England), 18(34).
Corrigan, P.W., Giffort, D., Mueser, K.T. 1999. Recovery as a psychological construct. Community and Mental Health, 35, 231-39.
Neil, S., Kilbride, M., and Pitt, L. 2009. The Questionnaire About the Process of Recorvery (QPR): a measurement tool developed in collaboration with service users. Psychosis, 1, 145-55.
Shanks, V., Williams, J., Leamy, M., Bird, V.J., LeBoutillier, C., and Slade, M. 2013. Measures of personal recovers: A systematic review. Psychiatric Services, 64(10), pp 974-80. doi: 10.1176/appi.ps.005012012.
Brevity of scale is an essential component in this patient group to achieve good response rates. Thus, despite the limited nature of the EQ-5D, it is recommended in this context. Although it has less extensive evidence than the SF-36, it does appear to have reasonable measurement characteristics (Oxford PROMS group 2009b). It also obtains much better response rates.
The SF-36 is the most widely validated measure of subjective health status in stroke and is also recommended. It has good psychometric properties in this group. However, it does not have high acceptability and has been noted to suffer from low response rates in these groups, and does not work well as a proxy measure (e.g. completed by family member) due to low agreement with results when completed by patient (Oxford PROMS group, n.d.).
For disease-specific measures, the SIPSO has been well used and is recommended. An alternative is the SF-SIS, which has been tested in the UK (see below).
Two stroke specific measures have been recommended. Whilst the Stroke Specific Quality of Life Scale (SSQOL) Is well validated, it has 49 items and therefore excluded. Whilst a SF-version has been developed (Post et al. 2011), we could not find any evidence of its use or validation in the UK context, and therefore prefer the SIS.
The SIS has been considered promising, but is long (59 items), and mostly USA/Australia and has previously therefore recommended cautiously for use in the UK (Oxford PROMS group, n.d.). However since the Oxford compendium, a Short-Form version has been developed and validated in the UK context and the 8-item SF-SIS can accurately provide the disability score and overall index score similar to the longer version, but with considerable brevity (C. Jenkinson et al. 2013).
The Subjective Index of Physical and Social Outcome (SIPSO)
The SIPSO has been validated as having good psychometric properties in two-independent evaluations (Boger, Demain, and Latter 2013; Oxford PROMS group, n.d.), and a further study identified it as meeting patient-centred criteria (Lawrence and Kinn 2012). It has been validated in several other languages. The 10-item SIS has been shown to be valid in UK context, including older stroke survivors (although the social subscore is unreliable in this patient group (Teale and Young 2015; Kersten et al. 2010)). In the UK, the SIPSO it has also been used to evaluate community based and exercise and education schemes (Harrington et al. 2010).
Whilst self-management is a fundamental component of person-centred care with stroke, a review found a lack of measures that were specifically for self-management of stroke, and that the existing measures that overlapped with this domain had questionable reliability and validity (Boger, Demain, and Latter 2013). Development of further measures was recommended. However, of the existing measures, the SIPSO (which also measures QoL) and Stroke Self Efficacy Questionnaire (SSEQ) are brief and have the best psychometric profile.
The Communication Outcome after Stroke Scale (COAST) is concerned with an individual patients perceptions of the effectiveness of their communication skills following stroke, and was identified in an independent review as meeting the patient-centred criteria of the study (Lawrence and Kinn 2012).
Rehabilitation measures are widely used for stoke, and are well-used by clinicians in the evaluation of stroke rehabilitation. However, they are typically designed for clinical judgement, and do not usually reflect issues of importance to patients. They are therefore beyond the scope of this project. However, the Barthel Index has been identified as having the strongest psychometric properties (Oxford PROMS group, n.d.) and is recommended for the period immediately following stroke (e.g not when LTC monitoring). The Frenchay Activities Index (FAI) and the Nottingham Extended ADL Scale are also well rated.
For a more detailed compendium of stroke specific measures, there is a stroke specific compendium:
http://www.strokengine.ca/assess/
Boger, Emma J., Sara Demain, and Sue Latter. 2013. “Self-Management: A Systematic Review of Outcome Measures Adopted in Self-Management Interventions for Stroke.” Disability and Rehabilitation 35 (17): 1415–28. doi:10.3109/09638288.2012.737080.
Harrington, R., G. Taylor, S. Hollinghurst, M. Reed, H. Kay, and V. A Wood. 2010. “A Community-Based Exercise and Education Scheme for Stroke Survivors: A Randomized Controlled Trial and Economic Evaluation.” Clinical Rehabilitation 24 (1): 3–15. doi:10.1177/0269215509347437.
Jenkinson, C., R. Fitzpatrick, H. Crocker, and M. Peters. 2013. “The Stroke Impact Scale: Validation in a UK Setting and Development of a SIS Short Form and SIS Index.” Stroke 44 (9): 2532–35. doi:10.1161/STROKEAHA.113.001847.
Kersten, Paula, Ann Ashburn, Steve George, and Joseph Low. 2010. “The Subjective Index for Physical and Social Outcome (SIPSO) in Stroke: Investigation of Its Subscale Structure.” BMC Neurology 10 (1): 26. doi:10.1186/1471-2377-10-26.
Lawrence, Maggie, and Sue Kinn. 2012. “Defining and Measuring Patient-Centred Care: An Example from a Mixed-Methods Systematic Review of the Stroke Literature.” Health Expectations: An International Journal of Public Participation in Health Care and Health Policy 15 (3): 295–326. doi:10.1111/j.1369-7625.2011.00683.x.
Oxford PROMS group. 2009. “A STRUCTURED REVIEW OF PATIENT-REPORTED OUTCOME MEASURES (PROMs) FOR DIABETES.” Oxford.
———. n.d. “STRUCTURED REVIEW OF PATIENT-REPORTED OUTCOME MEASURES (PROMs) FOR STROKE.” Oxford.
Post, M. W. M., H. Boosman, M. M. van Zandvoort, P. E. C. A. Passier, G. J. E. Rinkel, and J. M. A. Visser-Meily. 2011. “Development and Validation of a Short Version of the Stroke Specific Quality of Life Scale.” Journal of Neurology, Neurosurgery & Psychiatry 82 (3): 283–86. doi:10.1136/jnnp.2009.196394.
Teale, E. A., and J. B. Young. 2015. “A Patient Reported Experience Measure (PREM) for Use by Older People in Community Services.” Age and Ageing 44 (4): 667–72. doi:10.1093/ageing/afv014.
The Oxford PROMs group made the recommendation of either the SF-36 or EQ-5D as a generic PROM in heart disease (Oxford PROMS group 2009a), to be used alongside a disease-specific QoL measures for complementary evidence (see below). The SF-36 is the most frequently used measure in HF, but here is some controversy over the sensitivity of this instrument, particularly in advanced heart failure where the goal is to alleviate symptoms and limit progression. In such instances, the SF-36 may be subject to more floor and ceiling effects than the shorter SF-12, which may be acceptable as a shorter alternative. However, this instrument does not capture psychological domains, which is an important consideration in HF. In many instances, the EQ-5D is recommended alongside a longer, condition-specific PROM, given its brevity and the fact that it yields a UK-derived preference.
There are two measures that are widely used and well validated, with most reviews tending to favour the KCCQ and MLHFQ, with the CHFQ utilised in some circumstances (Oxford PROMS group 2009a; Garin et al. 2014; Garin et al. 2009):
KCCQ - Kansas City Cardiomyopathy Questionnaire
There are a large number of studies establishing evidence of reliability, validity, and responsiveness. A recent review scored this measure as having the best psychometric properties for HF QoL measures (Garin et al. 2014). Whilst this tool is somewhat longer than the main alternative (the MLHQF) it does cover domains that include social health and self-efficacy, whilst still being reasonably brief (23 items). We therefore recommend this tool as a disease-specific measure for HF.
MLHFQ - Minnesota Living with Heart Failure Questionnaire
The most widely used instrument for HRQoL in HF, with numerous studies establishing the validity and responsiveness of this measure (Garin et al. 2014; Oxford PROMS group 2009a).
However, both the KCCQ and MLHFQ do not cover the range of symptoms relevant for management of end-stage heart failure (particularly pain). In addition to the above two measures, there is also an individualised QoL measure available for HF:
CHFQ - Chronic Heart Failure Questionnaire
For patient management in clinical practice, this measure might be preferred if individualisation of impaired activities is relevant, whereby patients can select the most important activities for themselves. However, it cannot be self-administered and may be overly time consuming in most circumstances. It has a well-defined conceptual model and the better ability to detect change.
Garin, Olatz, Montse Ferrer, Angels Pont, Montserrat Rué, Anna Kotzeva, Ingela Wiklund, Eric Van Ganse, and Jordi Alonso. 2009. “Disease-Specific Health-Related Quality of Life Questionnaires for Heart Failure: A Systematic Review with Meta-Analyses.” Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation 18 (1): 71–85. doi:10.1007/s11136-008-9416-4.
Garin, Olatz, Michael Herdman, Gemma Vilagut, Montse Ferrer, Aida Ribera, Luis Rajmil, Jose M. Valderas, Francis Guillemin, Dennis Revicki, and Jordi Alonso. 2014. “Assessing Health-Related Quality of Life in Patients with Heart Failure: A Systematic, Standardized Comparison of Available Measures.” Heart Failure Reviews 19 (3): 359–67. doi:10.1007/s10741-013-9394-7.
Oxford PROMS group. 2009. “A STRUCTURED REVIEW OF PATIENT-REPORTED OUTCOME MEASURES FOR PEOPLE WITH HEART FAILURE: AN UPDATE 2009.”
The movement disorder taskforce previously reviewed Quality of Life scales in Parkinson’s disease (PD) (Martinez-Martin et al. 2011), in addition to an older review of measures (Marinus et al. 2002). A number of generic and disease specific measures were recommended.
The generic measures tend to suffer from floor/ceiling effects in Parkinson’s (Martinez-Martin et al. 2011). The SF-36 has been shown to be more responsive than some other generic QoL measures (Martinez-Martin et al. 2011), although the EQ-5D and NHP have also been recommended
PDQ Parkinson’s Disease Questionnaire
This is the most thoroughly tested and used HRQoL measure for PD, and has been recommended in two independent reviews (Martinez-Martin et al. 2011; Marinus et al. 2002). It possesses adequate psychometric properties and covers physical, mental and social domains. However, due to length, we also recommend the 8-item SF-PDQ which has also gained widespread use (Jenkinson et al. 1997).
21-item Parkinson’s Disease Quality of Life Questionnaire PDQL
This is the most frequently used PD HRQoL instrument. It has satisfactory psychrometric attributes but does not adequately cover self-care, sleep, cognition, close relationships and role functioning. It has been recommended by two independent reviews (Martinez-Martin et al. 2011; Marinus et al. 2002).
SCOPA - Scales for Outcomes in Parkinson’s Disease
Focused on Psychosocial adjustments rather than HRQoL and lacks physical and mental health domains. It does show satisfactory psychometrics (Martinez-Martin et al. 2011; Marinus et al. 2002).
Neuro-QoL health-related quality of life measurement system
Whilst not Parkinson’s-specific, the NeuoQoL measure (Gershon et al. 2012) was recently co-designed with patients and cares, measuring assess aspects of physical, mental, and social health. Initial findings show adequate psychometrics in PD (Nowinski et al. 2016), and it possesses characteristics, such as brevity, flexibility in administration, and suitability, for cross-disease comparisons that may be advantageous to users in a variety of settings. However, this measure still requires validation and testing within a UK context.
Gershon, Richard C., Jin Shei Lai, Rita Bode, Seung Choi, Claudia Moy, Tom Bleck, Deborah Miller, Amy Peterman, and David Cella. 2012. “Neuro-QOL: Quality of Life Item Banks for Adults with Neurological Disorders: Item Development and Calibrations Based upon Clinical and General Population Testing.” Quality of Life Research 21 (3): 475–86. doi:10.1007/s11136-011-9958-8.
Jenkinson, Crispin, Ray Fitzpatrick, Viv Peto, Richard Greenhall, and Nigel Hyman. 1997. “The PDQ-8: Development and Validation of a Short-Form Parkinson’s Disease Questionnaire.” Psychology & Health 12 (6): 805–14. doi:10.1080/08870449708406741.
Marinus, J., C. Ramaker, J. J. van Hilten, and A. M. Stiggelbout. 2002. “Health Related Quality of Life in Parkinson’s Disease: A Systematic Review of Disease Specific Instruments.” Journal of Neurology, Neurosurgery, and Psychiatry 72 (2): 241–48.
Martinez-Martin, Pablo, Martine Jeukens-Visser, Kelly E. Lyons, C. Rodriguez-Blazquez, Caroline Selai, Andrew Siderowf, Mickie Welsh, et al. 2011. “Health-Related Quality-of-Life Scales in Parkinson’s Disease: Critique and Recommendations.” Movement Disorders 26 (13): 2371–80. doi:10.1002/mds.23834.
Nowinski, Cindy J., Andrew Siderowf, Tanya Simuni, Catherine Wortman, Claudia Moy, and David Cella. 2016. “Neuro-QoL Health-Related Quality of Life Measurement System: Validation in Parkinson’s Disease: Neuro-Q o L Validation in PD.” Movement Disorders 31 (5): 725–33. doi:10.1002/mds.26546.
Systematic reviews identified that there is good evidence for reliability, validity and responsiveness for the SF-36, EQ-5D and NHP (Kirstie L. Haywood, Garratt, and Fitzpatrick 2006; K. L. Haywood, Garratt, and Fitzpatrick 2005; Oxford PROMS group, n.d.; Kirstie L. Haywood, Garratt, and Fitzpatrick 2006). The SF-36 is recommended where a detailed and broad ranging assessment of health is required, particularly in community dwelling older people with limited morbidity. In fact, and there is evidence that it may be more responsive than some older-person specific instruments such as the OMFAQ (see below).
The NHP (with dichotomous variables) has the highest response rate but lowest responsiveness, and also has items on social isolation. For older-person specific QoL instruments, the OMFAQ has been previously recommended (Kirstie L. Haywood, Garratt, and Fitzpatrick 2006). The EQ-5D is recommended where a more succinct assessment is required, particularly where a substantial change in health is expected. The EQ-5D and NHP may be more responsive where substantive changes in health are expected. Completion rates decline with increasing age and declining health status, and shorter instruments may be necessary in these contexts.
However, there is criticism is that it is not clear that older people were involved in item generation for generic instruments (Oxford PROMS group, n.d.), and that they may not cover many of the domains most relevant to the older population. Whilst many older person-specific tools have been used, the majority have just one published evaluation of their measurement properties. The three most commonly used older-person specific QoL tools are listed below, with EASY-Care being recommended in the UK context.
The Older Americans Resources and Services (OARS) methodology was designed to assess functional capacity in five dimensions (social resources, economic resources, mental health, physical health, and activities of daily living). It is the most widely evaluated specific instrument with (internationally) the most evidence. However, it has been criticised that the evidence of reliability was limited and responsiveness was poor, and has been chiefly used in the United States (K. L. Haywood, Garratt, and Fitzpatrick 2005). In contrast, the older people-specific CARE and EASY-Care (see below) are the most widely evaluated in the UK.
The Comprehensive Assessment and Referral Evaluation (CARE)
The Comprehensive Assessment and Referral Evaluation (CARE) was developed in the UK and the USA in the 1970s and 1980s for evaluating health and social concerns in older people. It is a semi-structured interview guide and an inventory of defined ratings. It is designated comprehensive because it covers psychiatric, medical, nutritional, economic and social problems rather than the interests of only one professional discipline. The style, scope and scoring of the CARE makes it suitable for use with both patients and non-patients, and a potentially useful aid in determining whether an elderly person should be referred, and to whom, for a health or social service. The CARE can also be employed in evaluating the effectiveness of that service if given
Elderly Assessment System (EASY-Care) was developed across Europe, including the UK, during the 1990s to provide a holistic and standardised approach to comprehensive geriatric assessment, and is a combined medical and social assessment tool. Development was supported by a grant from the European Regional Office of the World Health Organisation (WHO), involving cross-cultural adaptation and testing across several European countries. It is a comprehensive geriatric assessment (CGAs) tool. It has received numerous positive endorsements of acceptability from older people and practitioners, and it use is supported for individual needs assessment and care planning, but further research is needed for other uses (Craig et al. 2015). Completion by health-care professionals is preferred to self-completion (Craig et al. 2015).
It has 85 items across six domains - general health, physical abilities, memory, home, safety, and support, health-care services received, and looking after your health. Additional information about perceived needs, goal-setting, and satisfaction with care may also be gathered. Whilst the EASY-Care is long, it does have comprehensive domain coverage with fewer items than several of the more established instruments such as the OMFAQ and CARE, and thus offers comprehensive assessment in a reasonably economic manner.
There are a number of P3C tools available that are specifically designed to address the requirements of older people, including a sub-set of tools specifically designed for dementia. However, a recent review (Wilberforce et al. 2016) was unable to recommend any measure of person-centredness for use in older adult care, with the measures being undermined by the poor methodological quality underpinning them. The review suggested that observation-based measures such as DCM (see below) may be most suitable in contexts such as Dementia.
They can be divided into three broad categories – tools for home-care settings, tools for hospital settings, and tools for long-term care.
Recently, a pair of PREMs have been designed specifically to evaluate the delivery of person-centred care for older people in intermediate care services – one is designed for home services, the second (IC-PREM-Bed; see below) is designed for bed-based services. They have been developed and validated in the UK context (Teale and Young 2015) as part of the National Audit of Intermediate Care (NAIC). The tools were co-designed with a Delphi panel of experts and patients, with questions being modified to correspond with National Voices “I” statements. The IC-PREMs has been utilised across over 250 IC services audited by the NAIC. Although return rates were low (28% for the bed-based; 13% for the home IC-PREM), the bed-based rates are not dissimilar to other national surveys. Whilst the PREMs are designed to be used across a range of IC services for the purpose of local service improvement, the response scores from these measures can be combined so that an overall service experience score can be generated, suggesting that these measures could be used to facilitate within- and between-service comparisons in the future (Teale and Young 2015).
PAIEC - Patient assessment of integrated elderly care
The PAIEC is a recently developed version of the PACIC specifically designed for older populations (Uittenbroek et al. 2015). Similar to the PACIC, it has good coverage of a high number of important domains, including patient activation; delivery system design and decision support; goal setting and tailoring; problem-solving and contextual counselling; follow-up and coordination. However, it does not tap carer involvement, single point of contact/case manager and consistency of contact. Initial publication reveals it to be a and valid measurement instrument that evaluates quality of integrated care and support from the perspective of elderly people (Uittenbroek et al. 2015).
There are few alternatives within the context of UK home-based care for the elderly population, although the CCCQ (a short Dutch carer measure) may merit further investigation in the UK context.
A partner to the above mentioned IC-PREM-Home, this tool is a short measure that targets person-centred experience of bed-based intermediate care services for older people. Both measures are highlighted as ideal tools for use in the UK context.
One patient reported tool that has been used for older inpatients and used in a UK context is the P-CIS Patient-centred Inpatient Scale (P-CIS), although it has not been well validated or used. It is a tool that was developed to assess person centeredness in health care and tested it with a sample of hospital health care recipients (Coyle and Williams 2001). The tool measures recipient experiences of care and contains 20 items in five dimensions: personalization, empowerment, information, approachability/availability, and respectfulness. Strengths of the tool relate to it being short and concrete, and applicable to various settings. Potential weaknesses include unclear psychometric properties as estimates of validity and reliability are yet to be presented (Edvardsson and Innes 2010). Also, it cannot be ascertained if and how a systematic procedure guided by theory and statistics aided in the item selection process. Thus, the tool would benefit from further exploration.
The Individualised Care Scale (nurse and patient), which measures interactional modes of nursing, have been well used in UK context but have very little/unreliable psychometric testing in this context (Edvardsson and Innes 2010).
Other inpatient tools, such as the PCCQ (a Norwegian tool with patient and staff versions) which has been validated for older people in long term care facilities, but has not yet been used in the UK (Edvardsson and Innes 2010).
Whilst not tested in the UK context, the CCCQ was flagged by a recent review as one of the measures for older people that has been subject to most attempts to test measurement facilities, although the evidence is still patchy (Wilberforce et al. 2016) .
Most long-term care tools for older people are designed to evaluate dementia care services. As such, they are lengthy instruments which are completed by health care professionals. Only DCM has been used in the UK. Please see the “Dementia” shortlist for further information.
Dementia Care Mapping (DCM) is an audit tool that was designed to evaluate the quality of care of facilities. It is an observational tool with a patient-centred approach, using four predetermined coding frames that aim to make the observer view the world from the point of view of the person with dementia. Coding frames of DCMs are as follows: mood enhancers; behaviour categories; personal detractions and personal enhancers.
DCM was initially developed to help evaluate care in a practice development context. Over time, DCM has been used as an instrument to evaluate the impact of an intervention, to evaluate care, and as both an intervention and measure of outcome.
It has been argued that the strength of the tool is that it “ . . . may come closer to viewing QOL from the perspective of the person with dementia than many other available measures” (Sloane et al. 2007). It has widespread clinical appeal and is extensively used in dementia care practice, but it is time consuming, requires significant training (minimum 3 days), and is criticised over questions of its cost effectiveness (Edvardsson and Innes 2010). Furthermore, concerns about the reliability of DCM and its coding frames have been raised (Sloane et al. 2007), and it is a commercial product with restricted availability (Edvardsson and Innes 2010). However, there is a lack of credible alternatives to DCM for thorough evaluation or auditing of long-term dementia care. A systematic review of person-centred care for older people (Edvardsson and Innes 2010) did not identify any other long-term aged care tools that were UK focused (the PDC, P-CAT and Measures of Individualised care have not been used in the UK).
For patient reported measures, The P-CAT (a USA-based measure for HCPs) may be worth further investigation, flagged by a recent systematic review (Wilberforce et al. 2016) as having been subject to most attempts to test measurement facilities, although the evidence remains unsubstantial.
A recent policy document from AgeUK highlighted the impact of loneliness in the aging population on outcomes such as quality of life, mental and physical health, and earlier death (“Loneliness-Measurement-Guidance1.pdf,” n.d.). We failed to identify any P3C metrics that discussed loneliness, although some QoL measures (e.g. NHP) do have items related to social isolation.
For specifically measuring loneliness, a couple of well-used tools are recommended, with a newer tool having been developed for use by service providers in the UK context:
De Jong Gierveld Loneliness Scale
The Campaign to End Loneliness Measurement Tool (untested)
Other measures – such as proxy-reported (e.g. by family members) for psychological wellbeing in dementia – are beyond the scope of this work, but have been recently reviewed (Ellis-Smith et al. 2016).
Other issues in the quantitative assessment of the elderly population – such as the Frailty Index (FI) (Drubbel et al. 2014) are beyond the scope of this shortlist, although they do have utility as a complementary measure alongside patient reported measures. In addition, there are a number of patient-reported tools for frailty, which are discussed further in recent reviews (Bouillon et al. 2013; de Vries et al. 2011).
Please refer to our dementia-specific shortlist on the tab above.
Bouillon, Kim, Mika Kivimaki, Mark Hamer, Severine Sabia, Eleonor I. Fransson, Archana Singh-Manoux, Catharine R. Gale, and G. David Batty. 2013. “Measures of Frailty in Population-Based Studies: An Overview.” BMC Geriatrics 13: 64. doi:10.1186/1471-2318-13-64.
Coyle, J., and B. Williams. 2001. “Valuing People as Individuals: Development of an Instrument through a Survey of Person-Centredness in Secondary Care.” Journal of Advanced Nursing 36 (3): 450–59.
Craig, C., N. Chadborn, G. Sands, H. Tuomainen, and J. Gladman. 2015. “Systematic Review of EASY-Care Needs Assessment for Community-Dwelling Older People.” Age and Ageing 44 (4): 559–65. doi:10.1093/ageing/afv050.
de Vries, N. M., J. B. Staal, C. D. van Ravensberg, J. S. M. Hobbelen, M. G. M. Olde Rikkert, and M. W. G. Nijhuis-van der Sanden. 2011. “Outcome Instruments to Measure Frailty: A Systematic Review.” Ageing Research Reviews 10 (1): 104–14. doi:10.1016/j.arr.2010.09.001.
Drubbel, Irene, Mattijs E. Numans, Guido Kranenburg, Nienke Bleijenberg, Niek J. de Wit, and Marieke J. Schuurmans. 2014. “Screening for Frailty in Primary Care: A Systematic Review of the Psychometric Properties of the Frailty Index in Community-Dwelling Older People.” BMC Geriatrics 14: 27. doi:10.1186/1471-2318-14-27.
Edvardsson, David, and Anthea Innes. 2010. “Measuring Person-Centered Care: A Critical Comparative Review of Published Tools.” The Gerontologist 50 (6): 834–46. doi:10.1093/geront/gnq047.
Ellis-Smith, Clare, Catherine J. Evans, Anna E. Bone, Lesley A. Henson, Mendwas Dzingina, Pauline M. Kane, Irene J. Higginson, and Barbara A. Daveson. 2016. “Measures to Assess Commonly Experienced Symptoms for People with Dementia in Long-Term Care Settings: A Systematic Review.” BMC Medicine 14: 38. doi:10.1186/s12916-016-0582-x.
Haywood, K. L., A. M. Garratt, and R. Fitzpatrick. 2005. “Quality of Life in Older People: A Structured Review of Generic Self-Assessed Health Instruments.” Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation 14 (7): 1651–68.
Haywood, Kirstie L., Andrew M. Garratt, and Ray Fitzpatrick. 2006. “Quality of Life in Older People: A Structured Review of Self-Assessed Health Instruments.” Expert Review of Pharmacoeconomics & Outcomes Research 6 (2): 181–94. doi:10.1586/14737167.6.2.181.
“Loneliness-Measurement-Guidance1.pdf.” n.d.
Oxford PROMS group. n.d. “Health Status and Quality of Life in Older People: A Review.” Oxford.
Sloane, Philip D., Dawn Brooker, Lauren Cohen, Carolinda Douglass, Perry Edelman, Bradley R. Fulton, Shannon Jarrott, et al. 2007. “Dementia Care Mapping as a Research Tool.” International Journal of Geriatric Psychiatry 22 (6): 580–89. doi:10.1002/gps.1721.
Teale, E. A., and J. B. Young. 2015. “A Patient Reported Experience Measure (PREM) for Use by Older People in Community Services.” Age and Ageing 44 (4): 667–72. doi:10.1093/ageing/afv014.
Wilberforce, Mark, David Challis, Linda Davies, Michael P. Kelly, Chris Roberts, and Nik Loynes. 2016. “Person-Centredness in the Care of Older Adults: A Systematic Review of Questionnaire-Based Scales and Their Measurement Properties.” BMC Geriatrics 16 (1). doi:10.1186/s12877-016-0229-y.
A recent review of QoL measures specifically designed for dementia failed to find a measure that could be recommended as generally applicable, concluding that a broader (and more rigorously tested) dementia-specific QoL measure was required (Bowling et al. 2015). Moreover, a recent publication has argued that the EQ-5D might have advantages over other dementia-specific measures (see below), and it could be used routinely as a stand-alone measure of QoL in dementia research (Aguirre et al. 2016). Indeed, other generic measures have been used to measure QoL in Dementia, with the HUI3 being used to measure every stage of dementia (Ettema et al. 2005). Nonetheless, the content validity of generic QoL measures has been seriously questioned in relation to dementia, supporting the general preference for disease specific measures (Ettema et al. 2005). The two most commonly used – both validated in the UK – are:
Reviews have highlighted the Quality of Life in Alzheimer’s Disease (QOL-AD) (Bowling et al. 2015) with a version for both the person with dementia and the carer. It is the measure of choice for dementia-specific QoL, as it is brief (13 items) and has evidence of psychometric acceptability, sensitivity to psychosocial interventions, and can be used with people with poor cognitive scores (as low as 3 on the Mini Mental State Examination) (Moniz-Cook et al. 2008)
The main alternative is the Dementia Quality of Life (DQOL), which has a broader model of QoL that may be preferred when more details about QoL are required. However, it may appear repetitive to respondents and the self-rated version is limited to people with mild to moderate dementia (Bowling et al. 2015).
Other measures
Further QoL specific measures for dementia can be found in reviews (Bowling et al. 2015). Other measures – such as those for psychological wellbeing in dementia – are beyond the scope of this work, but have been recently reviewed (Ellis-Smith et al. 2016).
There are a number of long-term care tools for older people are designed to evaluate dementia care services. These are lengthy instruments which are completed by health care professionals. Only the DCM has been used in the UK.
Dementia Care Mapping (DCM) is an audit tool that was designed to evaluate the quality of care in long-term care facilities. It is an observational tool with a patient-centred approach, using four predetermined coding frames that aim to make the observer view the world from the point of view of the person with dementia. Coding frames of DCMs are as follows: mood enhancers; behaviour categories; personal detractions and personal enhancers.
DCM was initially developed to help evaluate care in a practice development context. Over time, DCM has been used as an instrument to evaluate the impact of an intervention, to evaluate care, and as both an intervention and measure of outcome.
It has been argued that the strength of the tool is that, it “ . . . may come closer to viewing QOL from the perspective of the person with dementia than many other available measures” (Sloane et al. 2007). It has widespread clinical appeal and is extensively used in dementia care practice, but it is time consuming, requires significant training (minimum 3 days), and is criticised over questions of its cost effectiveness (Edvardsson and Innes 2010). Furthermore, concerns about the reliability of DCM and its coding frames have been raised (Sloane et al. 2007), and it is a commercial product with restricted availability (Edvardsson and Innes 2010). However, there is a lack of credible alternatives to DCM for thorough evaluation of auditing of long-term dementia care.
The Family Perception of Care Scale was developed for generic use in long-term care facilities (Vohra et al. 2004). It consists of four subscales: resident care, family support, communication, and rooming. It was deemed as having excellent content validity, covering all essential domains of palliative care, is simple to administer and scored well in systematic review of psychometric properties (Parker and Hodgkinson 2011). A more recent review of End of Life care revealed it to have reasonable (but not complete) domain coverage (Lendon et al. 2015). Furthermore, a review of end-of-life care in dementia deemed it as one of the top two tools valid and reliable for measuring quality of care in this population (van Soest-Poortvliet et al. 2012). However, this tool has not been used and validated in a UK context.
A systematic review for assessing end-of-life in people with dementia (van Soest-Poortvliet et al. 2012) found the most valid and reliable were the EOLD-SWC & EOLD–CAD (see below), followed by the FPCS (above).
Satisfaction with Care at the End-of-Life in Dementia (EOLD-SWC) & Comfort Assessment in Dying with Dementia (EOLD-CAD). EOLD-CAD has four subscales: Physical Distress, Dying Symptoms, Emotional Distress, and Well Being. Both scales have been deemed as being the most valid and internally consistent for end-of-life in dementia in systematic review (van Soest-Poortvliet et al. 2012) Furthermore, this set of scales was recently signposted in a UK a publication of implementation of UK national policies (Candy et al. 2015), although we could not find publications discussing validation of the scales in a UK context.
Informal carers make an increasingly integral contribution to care deliver, and policy makers are putting more emphasis on informal carers. A recent review of tools for assessing the impact of informal care on carers found 24 measures, with the number of such tools increasingly rapidly over recent years (Michels et al. 2016). One is aimed specifically at carers of dementia patients:
The ZBI is a tool a tool to measure the impact of dementia caregiving on the carer, and has been used in a UK context (Cerga-Pashoja et al. 2010).
Aguirre, Elisa, Sujin Kang, Zoe Hoare, Rhiannon Tudor Edwards, and Martin Orrell. 2016. “How Does the EQ-5D Perform When Measuring Quality of Life in Dementia against Two Other Dementia-Specific Outcome Measures?” Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation 25 (1): 45–49. doi:10.1007/s11136-015-1065-9.
Bowling, Ann, Gene Rowe, Sue Adams, Paula Sands, Kritika Samsi, Maureen Crane, Louise Joly, and Jill Manthorpe. 2015. “Quality of Life in Dementia: A Systematically Conducted Narrative Review of Dementia-Specific Measurement Scales.” Aging & Mental Health 19 (1): 13–31. doi:10.1080/13607863.2014.915923.
Candy, Bridget, Margaret Elliott, Kirsten Moore, Victoria Vickerstaff, Elizabeth Sampson, and Louise Jones. 2015. “UK Quality Statements on End of Life Care in Dementia: A Systematic Review of Research Evidence.” BMC Palliative Care 14 (1). doi:10.1186/s12904-015-0047-6.
Cerga-Pashoja, Arlinda, David Lowery, Rahul Bhattacharya, Mark Griffin, Steve Iliffe, James Lee, Claire Leonard, et al. 2010. “Evaluation of Exercise on Individuals with Dementia and Their Carers: A Randomised Controlled Trial.” Trials 11: 53. doi:10.1186/1745-6215-11-53.
Edvardsson, David, and Anthea Innes. 2010. “Measuring Person-Centered Care: A Critical Comparative Review of Published Tools.” The Gerontologist 50 (6): 834–46. doi:10.1093/geront/gnq047.
Ellis-Smith, Clare, Catherine J. Evans, Anna E. Bone, Lesley A. Henson, Mendwas Dzingina, Pauline M. Kane, Irene J. Higginson, and Barbara A. Daveson. 2016. “Measures to Assess Commonly Experienced Symptoms for People with Dementia in Long-Term Care Settings: A Systematic Review.” BMC Medicine 14: 38. doi:10.1186/s12916-016-0582-x.
Ettema, Teake P., Rose-Marie Dröes, Jacomine de Lange, Gideon J. Mellenbergh, and Miel W. Ribbe. 2005. “A Review of Quality of Life Instruments Used in Dementia.” Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation 14 (3): 675–86.
Lendon, Jessica Penn, Sangeeta C. Ahluwalia, Anne M. Walling, Karl A. Lorenz, Oluwatobi A. Oluwatola, Rebecca Anhang Price, Denise Quigley, and Joan M. Teno. 2015. “Measuring Experience With End-of-Life Care: A Systematic Literature Review.” Journal of Pain and Symptom Management 49 (5): 904–915.e3. doi:10.1016/j.jpainsymman.2014.10.018.
Michels, Charlotte T. J., Mary Boulton, Astrid Adams, Bee Wee, and Michele Peters. 2016. “Psychometric Properties of Carer-Reported Outcome Measures in Palliative Care: A Systematic Review.” Palliative Medicine 30 (1): 23–44. doi:10.1177/0269216315601930.
Moniz-Cook, E., M. Vernooij-Dassen, R. Woods, F. Verhey, R. Chattat, M. De Vugt, G. Mountain, et al. 2008. “A European Consensus on Outcome Measures for Psychosocial Intervention Research in Dementia Care.” Aging & Mental Health 12 (1): 14–29. doi:10.1080/13607860801919850.
Parker, Deborah, and Brent Hodgkinson. 2011. “A Comparison of Palliative Care Outcome Measures Used to Assess the Quality of Palliative Care Provided in Long-Term Care Facilities: A Systematic Review.” Palliative Medicine 25 (1): 5–20. doi:10.1177/0269216310378786.
Sloane, Philip D., Dawn Brooker, Lauren Cohen, Carolinda Douglass, Perry Edelman, Bradley R. Fulton, Shannon Jarrott, et al. 2007. “Dementia Care Mapping as a Research Tool.” International Journal of Geriatric Psychiatry 22 (6): 580–89. doi:10.1002/gps.1721.
van Soest-Poortvliet, Mirjam C., Jenny T. van der Steen, Sheryl Zimmerman, Lauren W. Cohen, Maartje S. Klapwijk, Mirjam Bezemer, Wilco P. Achterberg, Dirk L. Knol, Miel W. Ribbe, and Henrica C. W. de Vet. 2012. “Psychometric Properties of Instruments to Measure the Quality of End-of-Life Care and Dying for Long-Term Care Residents with Dementia.” Quality of Life Research?: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation 21 (4): 671–84. doi:10.1007/s11136-011-9978-4.
Vohra, Julie Uma, Kevin Brazil, Steven Hanna, and Julia Abelson. 2004. “Family Perceptions of End-of-Life Care in Long-Term Care Facilities.” Journal of Palliative Care 20 (4): 297–302.
Measurement of quality and experience of palliative care presents unique challenges. In many instances, self-reporting is ideal, but issues of cognitive impairment and increasing frailty often render this impossible. Therefore, proxy measures (responded by professional or non-professional carers) are often necessary, with some measures having multiple versions for each respondent, sometimes taking a “toolkit” approach. A comprehensive introduction and guidance to measurement in palliative care has been published (PRISMA 2008).
There have been nine outcome domains proposed for the evaluation of End of Life care (Mularski et al. 2007; Parker and Hodgkinson 2011). Many of these domains coincide with general principles of P3C care. They are:
(1) symptom management
(2) whole person and maintaining quality of life;
(3) functional aspects;
(4) satisfaction;
(5) relationships;
(6) decision making and care planning;
(7) continuity and communication;
(8) family burden and well being;
(9) quality of death and end-of-life experience
However, tools for measuring patient experience and P3C in end-of-life care is highly fragmented, with no “standout” tools that can be easily recommended for all contexts. A recent review of experience measures for end-of-life experiences of care identified a total of 51 unique instruments, the majority of which used the carer as the respondent (Lendon et al. 2015). There was considerable variation in the surveys in terms of health care settings, context, modes of administration and identification of proxies, with most having a narrow scope. Only 12 of the 51 had been published in more than one article. Some of the most important measures are:
Palliative Care Outcome Scale - POS
The POS was developed in 1999. This measure was designed for use with advanced cancer patients as a standardized measure to evaluate the work of palliative care support teams. It measures items including symptoms, anxiety and insight, family anxiety and insight, quality of communication with health care professionals and carers, and the need for practical support. It has grown into a family of tools that includes versions for healthcare professionals, patients and carers. They are validated instruments that can be used in clinical care, audit, research and training.
It is among the top five outcome measures used in research as well as clinical care and audit in Europe, is used worldwide and has been translated into numerous languages, and is increasingly being used as an evaluation tool (Collins et al. 2015), in addition to assessing patients symptoms and needs. There is a growing body of evidence for the validity of the POS and its acceptability among patients, caregivers, and health professionals. It is largely considered as a valid and reliable tool (Collins et al. 2015), although other systematic reviews have noted a lack of established psychometric properties (Albers et al. 2010). Furthermore, the POS has received criticism in review when used in context of long-term care facilities. This was due to problems of reporting of psychometric data, and issues with many of the questions. Further refinement and psychometric testing of the POS for long-term care facilities being recommended (Parker and Hodgkinson 2011). It has similarly been criticised for poor performance in dementia populations (van Soest-Poortvliet et al. 2012), and specialised tools may be required in these contexts.
The POS measures are specifically developed for use among people severely affected by diseases such as cancer, respiratory, heart, renal or liver failure, and neurological diseases. Extended versions of the POS-S have been developed for use with those living with multiple sclerosis (POS-S-MS) (Sleeman and Higginson 2013), Parkinson’s disease (POS-S-PP) (which has also been used for motor neuron disease (Hughes et al. 2004)), and end-stage renal disease(POS-S-renal).
After-death Bereaved Family Member Interview - ADBFI
The ADBFI is a survey administered by proxy interview to measure quality of care at the end of life from the perspective of family members. It is based on a patient-focused, family-centred approach that examines whether end-of-life care meets the expectations and needs of the dying person and their family members. The instrument investigates a comprehensive coverage of domains, including whether physical comfort and emotional support are provided to the dying patient, whether shared decision making is promoted, if care is focused on the individual, whether the needs of family members are met and if satisfactory coordination of care is achieved.
This was the most cited of measures in a recent systematic review for measuring EoL experience (Lendon et al. 2015). It covers a broad variety of domains and has been used in the widest contexts, including nursing homes, hospitals, cancer centres and outpatient services for diseases including cancer and dementia. It has been adapted and use in a UK trial (West et al. 2014).
The interview has been expanded into a toolkit (also known as “TIME”) (Teno et al. 2001), with the authors having published a very useful resource guide to evaluating end-of-life care:
https://nts122.chcr.brown.edu/pcoc/resourceguide/resourceguide.pdf
Family Evaluation of Hospice Care - FEHC
The FEHC is an online survey tool that captures patient and family experiences at the end of life, allowing hospice programs to contribute their data to a repository for the purpose of benchmarking and research. It is based on the ADBFI (see above). It has been adapted for Irish (McKeown et al. 2015), but not UK context.
Quality of Dying and Death - QODD
The QODD was constructed from concepts elicited from literature review, qualitative interviews with persons with and without chronic and terminal conditions. It has six conceptual domains: symptoms and personal care, preparation for death, moment of death, family, treatment preferences, and whole person concerns. It is a briefer tool that the ADBFI, but still retaining very good domain coverage. Furthermore, unlike many other tools, the respondent can be either the carer of a healthcare professional. A systematic review deemed it one of the most psychometrically sound tools for measuring quality of death (Albers et al. 2010), although it has not yet been used in a UK context.
However, a systematic review of tools for long-term care facilities argued that the QODD was not specifically developed or tested in LTC facilities and many of the questions are not appropriate for this setting (Parker and Hodgkinson 2011). Tools that have been recommended in this context are discussed below.
The McGill Quality of Life Questionnaire MQOL is a patient-reported Quality of Life tool for people with life threatening illness (Cohen et al. 1997). It is one of the most used scales in the USA, although we could not find evidence of validation in a UK context. It was recommended as the scale with the best overall psychometric properties for QoL in palliative care in a previous systematic review (Albers et al. 2010).
The QUAL-E is a brief measure of quality of life at the end of life. It was recommended as one of the scales with the best overall psychometric properties for QoL in palliative care in a previous systematic review (Albers et al. 2010).
For use in long-term care facilities, a systematic review identified ten measures that were appropriate for long-term care facilities, including the well-used and validated QODD, POS and ADBFI (listed above). The POS and ADBFI are a comprehensive suite of measures, and both have both been used in a UK context. However the FPCS (see below) was considered by the authors as the most preferred scale, with the ADBFI toolkit also being recommended, although these measures require adaptation and testing in a UK context.
The Family Perception of Care Scale was developed exclusively with long-term care facilities (Vohra et al. 2004). It consists of four subscales: resident care, family support, communication, and rooming. It was deemed as having excellent content validity, covering all essential domains of palliative care, is simple to administer and scored well in systematic review of psychometric properties (Parker and Hodgkinson 2011). A more recent review of End of Life care revealed it to have reasonable (but not complete) domain coverage (Lendon et al. 2015). Furthermore, a review of end-of-life care in dementia deemed it as one of the top two tools valid and reliable for measuring quality of care in this population (van Soest-Poortvliet et al. 2012). However, this tool has not been used and validated in a UK context.
A systematic review for assessing end-of-life in people with dementia (van Soest-Poortvliet et al. 2012) found the most valid and reliable measures were the EOLD-SWC & EOLD–CAD (see below), followed by the FPCS (above).
EOLD–SWC & EOLD-CAD
Satisfaction with Care at the End-of-Life in Dementia (EOLD-SWC) & Comfort Assessment in Dying with Dementia (EOLD-CAD). EOLD-CAD has four subscales: Physical Distress, Dying Symptoms, Emotional Distress, and Well Being. Both scales have been deemed as being the most valid and internally consistent for end-of-life in dementia in a systematic review (van Soest-Poortvliet et al. 2012) Furthermore, this set of scales was recently signposted in a UK publication of implementation of UK national policies (Candy et al. 2015), although we could not find publications discussing validation of the scales in a UK context.
Informal carers make an increasingly integral contribution to care deliver, and policy makers are putting more emphasis on informal carers. A recent review of tools for assessing the impact of informal care on carers found 24 measures, with the number of such tools increasing rapidly over recent years (Michels et al. 2016). An earlier systematic review identified 62 instruments that had some utility in this context, although not all were specific to palliative care (P. L. Hudson et al. 2010). The two most commonly used tools to assess carers were:
The CRA is a multidimensional instrument to assess the reactions of family members caring for elderly persons with physical impairments, Alzheimer's disease, and cancer. However, despite being the most widely used carer-specific measure, limited psychometric information has been reported.
The ZBI is a tool a tool to measure the impact of dementia caregiving on the carer, and has been used in a UK context (Cerga-Pashoja et al. 2010).
Albers, Gwenda, Michael A. Echteld, Henrica C. W. de Vet, Bregje D. Onwuteaka-Philipsen, Mecheline H. M. van der Linden, and Luc Deliens. 2010. “Evaluation of Quality-of-Life Measures for Use in Palliative Care: A Systematic Review.” Palliative Medicine 24 (1): 17–37. doi:10.1177/0269216309346593.
Candy, Bridget, Margaret Elliott, Kirsten Moore, Victoria Vickerstaff, Elizabeth Sampson, and Louise Jones. 2015. “UK Quality Statements on End of Life Care in Dementia: A Systematic Review of Research Evidence.” BMC Palliative Care 14 (1). doi:10.1186/s12904-015-0047-6.
Cerga-Pashoja, Arlinda, David Lowery, Rahul Bhattacharya, Mark Griffin, Steve Iliffe, James Lee, Claire Leonard, et al. 2010. “Evaluation of Exercise on Individuals with Dementia and Their Carers: A Randomised Controlled Trial.” Trials 11: 53. doi:10.1186/1745-6215-11-53.
Cohen, S. R., B. M. Mount, E. Bruera, M. Provost, J. Rowe, and K. Tong. 1997. “Validity of the McGill Quality of Life Questionnaire in the Palliative Care Setting: A Multi-Centre Canadian Study Demonstrating the Importance of the Existential Domain.” Palliative Medicine 11 (1): 3–20.
Collins, Emily S., Jana Witt, Claudia Bausewein, Barbara A. Daveson, Irene J. Higginson, and Fliss E.M. Murtagh. 2015. “A Systematic Review of the Use of the Palliative Care Outcome Scale and the Support Team Assessment Schedule in Palliative Care.” Journal of Pain and Symptom Management 50 (6): 842–853.e19. doi:10.1016/j.jpainsymman.2015.07.015.
Hudson, Peter L., Tom Trauer, Suzanne Graham, Gunn Grande, Gail Ewing, Sheila Payne, Kelli I. Stajduhar, and Kristina Thomas. 2010. “A Systematic Review of Instruments Related to Family Caregivers of Palliative Care Patients.” Palliative Medicine 24 (7): 656–68. doi:10.1177/0269216310373167.
Hughes, Rhidian A., Fiona Aspinal, Irene J. Higginson, Julia M. Addington-Hall, Maria Dunckley, Christina Faull, and Anu Sinha. 2004. “Assessing Palliative Care Outcomes for People with Motor Neurone Disease Living at Home.” International Journal of Palliative Nursing 10 (9): 449–53. doi:10.12968/ijpn.2004.10.9.16051.
Lendon, Jessica Penn, Sangeeta C. Ahluwalia, Anne M. Walling, Karl A. Lorenz, Oluwatobi A. Oluwatola, Rebecca Anhang Price, Denise Quigley, and Joan M. Teno. 2015. “Measuring Experience With End-of-Life Care: A Systematic Literature Review.” Journal of Pain and Symptom Management 49 (5): 904–915.e3. doi:10.1016/j.jpainsymman.2014.10.018.
McKeown, Kieran, Trutz Haase, Jonathan Pratschke, Shelagh Twomey, Helen Donovan, and Feline Engling. 2015. “Determinants of Care Outcomes for Patients Who Die in Hospital in Ireland: A Retrospective Study.” BMC Palliative Care 14: 11. doi:10.1186/s12904-015-0014-2.
Michels, Charlotte T. J., Mary Boulton, Astrid Adams, Bee Wee, and Michele Peters. 2016. “Psychometric Properties of Carer-Reported Outcome Measures in Palliative Care: A Systematic Review.” Palliative Medicine 30 (1): 23–44. doi:10.1177/0269216315601930.
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Parker, Deborah, and Brent Hodgkinson. 2011. “A Comparison of Palliative Care Outcome Measures Used to Assess the Quality of Palliative Care Provided in Long-Term Care Facilities: A Systematic Review.” Palliative Medicine 25 (1): 5–20. doi:10.1177/0269216310378786.
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Sleeman, Katherine E., and Irene J. Higginson. 2013. “A Psychometric Validation of Two Brief Measures to Assess Palliative Need in Patients Severely Affected by Multiple Sclerosis.” Journal of Pain and Symptom Management 46 (3): 406–12. doi:10.1016/j.jpainsymman.2012.08.007.
Teno, J. M., B. Clarridge, V. Casey, S. Edgman-Levitan, and J. Fowler. 2001. “Validation of Toolkit After-Death Bereaved Family Member Interview.” Journal of Pain and Symptom Management 22 (3): 752–58.
van Soest-Poortvliet, Mirjam C., Jenny T. van der Steen, Sheryl Zimmerman, Lauren W. Cohen, Maartje S. Klapwijk, Mirjam Bezemer, Wilco P. Achterberg, Dirk L. Knol, Miel W. Ribbe, and Henrica C. W. de Vet. 2012. “Psychometric Properties of Instruments to Measure the Quality of End-of-Life Care and Dying for Long-Term Care Residents with Dementia.” Quality of Life Research?: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation 21 (4): 671–84. doi:10.1007/s11136-011-9978-4.
Vohra, Julie Uma, Kevin Brazil, Steven Hanna, and Julia Abelson. 2004. “Family Perceptions of End-of-Life Care in Long-Term Care Facilities.” Journal of Palliative Care 20 (4): 297–302.
West, Emily, Vittoria Romoli, Silvia Di Leo, Irene J. Higginson, Guido Miccinesi, and Massimo Costantini. 2014. “Feasibility of Assessing Quality of Care at the End of Life in Two Cluster Trials Using an after-Death Approach with Multiple Assessments.” BMC Palliative Care 13: 36. doi:10.1186/1472-684X-13-36.