Measurement of patient-reported outcomes. 2: Are current measures failing us?

McKenna, Stephen P.; Heaney, Alice; Wilburn, Jeanette

doi:10.1080/13696998.2018.1560304

Cited by 33 publications

(41 citation statements)

References 59 publications

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“…Limited use of PROMs data has previously been observed in initiatives for implementing PROMs in clinical practice [ 23 ]. Proposed explanations include a perception that the collected data are not fit for purpose, and that there are challenges associated with accessing, understanding and acting on the results [ 19 ]. Several authors have emphasised the importance of the collected data being perceived as relevant and meaningful by the health care personnel who invest their time and effort to collecting it [ 5 , 20 , 21 , 53 ].…”

Section: Discussionmentioning

confidence: 99%

“…The implementation and use of PROMs in clinical practice is associated with several challenges [16][17][18][19][20][21]. These challenges include: the patients' willingness to respond to topics addressed in different PROMs; choosing the most adequate PROM; interpreting results; skepticism regarding their usefulness and associated workload; and how the measurements fit into the way the care is organised, including (IT) infrastructure.…”

Section: Introductionmentioning

confidence: 99%

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Collection and use of EQ-5D for follow-up, decision-making, and quality improvement in health care - the case of the Swedish National Quality Registries

Ernstsson

Janssen

Heintz

2020

J Patient Rep Outcomes

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Background The Swedish National Quality Registries (NQRs) contain individual-level health care data for specific patient populations, or patients receiving specific interventions. Approximately 90% of the 105 Swedish NQRs include any patient-reported outcome measure, with EQ-5D being the most common. As there has been no general overview of EQ-5D data within the NQRs, this study fills a knowledge gap by reporting how the data are collected, presented, and used at different levels of the Swedish health care system. Methods All 46 NQRs with a license for the use of EQ-5D were included. Information was retrieved from the registries’ annual reports or from websites, using a template that was subsequently sent to each registry for completion and confirmation. If considered necessary, the contact was followed-up with an interview, either in-person or over the telephone. The uses of EQ-5D were categorised as denoting usage for follow-up, decision-making, or quality improvement in Swedish health care. Results In total, 41 of the 46 licensed registries reported collection of EQ-5D data. EQ-5D is most commonly collected within registries related to the musculoskeletal system, but it has a wide application also in other disease areas. Thirty-six registries provide EQ-5D results to patients, clinicians, or other decision-makers. Twenty-two of the registries reported that EQ-5D data are being used for follow-up, decision-making or quality improvement. The registries most commonly reported use of data for assessing interventions, and in quality indicators to follow-up the quality of care at a national level. Conclusion Collection and use of EQ-5D data vary across the Swedish NQRs, which may partly be accounted for by the different purposes of the registries. The provided examples of use illustrate how EQ-5D data can inform decisions at different levels of the health care system. However, there is potential for improving the use of EQ-5D data.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Collection and use of EQ-5D for follow-up, decision-making, and quality improvement in health care - the case of the Swedish National Quality Registries

Ernstsson

Janssen

Heintz

2020

J Patient Rep Outcomes

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“…Not only can it cause the outcomes of clinical trials to be misleading, incorrect treatment decisions may also result. McKenna and colleagues [62] have described in detail how the use of composite PROMs can adversely affect clinical practice.…”

Section: Intermediate Endpointsmentioning

confidence: 99%

Composite outcome measurement in clinical research: the triumph of illusion over reality?

McKenna

Heaney

2020

Journal of Medical Economics

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Composite measures that combine different types of indicators are widely used in medical research; to evaluate health systems, as outcomes in clinical trials and patient-reported outcome measurement. The potential advantages of such indices are clear. They are used to summarise complex data and to overcome the problem of evaluating new interventions when the most important outcome is rare or likely to occur far in the future. However, many scientists question the value of composite measures, primarily due to inadequate development methodology, lack of transparency or the likelihood of producing misleading results. It is argued that the real problems with composite measurement are related to their failure to take account of measurement theory and the absence of coherent theoretical models that justify the addition of the individual indicators that are combined into the composite index. All outcome measures must be unidimensional if they are to provide meaningful data. They should also have dimensional homogeneity. Ideally, a specification equation should be developed that can predict accurately how organisations or individuals will score on an index, based on their scores on the individual indicators that make up the measure. The article concludes that composite measures should not be used as they fail to apply measurement theory and, consequently, produce invalid and misleading scores.

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“…A patient-centric approach, where life maintains its quality if patient needs are fulfilled, is not a new concept. It was first proposed in the early 1990s and has been the driving force in disease-specific instrument development within the Rasch measurement framework 12,13 .…”

Section: Need Fulfillment and Raschmentioning

confidence: 99%

Measurement, modeling and QALYs

Langley

McKenna

2020

F1000Res

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Over the past 30 years, a mainstay of health technology assessment has been the creation of modeled incremental cost-per-quality adjusted life year (QALY) claims. These are intended to inform resource allocation decisions. Unfortunately, the reliance on the construction of QALYs from generic utility scales is misplaced. Those advocating QALY-based lifetime modeled claims fail to appreciate the limitations placed on these constructs by the axioms of fundamental measurement. Utility scales, such as those created by the EQ-5D-3L instrument, are nothing more than multidimensional, ordinal scales. Such scales cannot support basic arithmetic operations. Interval scales can support addition and subtraction; ratio scales the further operations of multiplication and division. Those who advocate the construction of QALYs fail to appreciate that such an operation is only possible if the utility scale is unidimensional and has ratio properties with a true zero. The utility measures available do not meet these requirements. As we cannot produce meaningful utility values, the QALY is an invalid construct. Consequently, cost-per-incremental QALY claims are impossible to sustain and the application of cost-per QALY thresholds meaningless. As utility is a latent, unidimensional variable, the best a measure of utility could achieve would be unidimensionality and interval scaling properties. Where such measures are available, they could support claims for response to therapy. Consequently, there would be no need to continue constructing imaginary lifetime value assessment frameworks. Admitting that the QALY is a fatally flawed construct means rejecting 30 years of cost-per-QALY models.

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Measurement of patient-reported outcomes. 2: Are current measures failing us?

Cited by 33 publications

References 59 publications

Collection and use of EQ-5D for follow-up, decision-making, and quality improvement in health care - the case of the Swedish National Quality Registries

Collection and use of EQ-5D for follow-up, decision-making, and quality improvement in health care - the case of the Swedish National Quality Registries

Composite outcome measurement in clinical research: the triumph of illusion over reality?

Measurement, modeling and QALYs

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