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Assuring quality in person-centred healthcare
RISE - Research Institutes of Sweden, Safety and Transport, Measurement Science and Technology.ORCID iD: 0000-0003-4349-500X
2017 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

In assuring quality in areas of grand challenge – such as healthcare – at present undergoing radical changes, one might aspire to the same kind of quality-assurance of social measurements as is established in physics and engineering. Is this realistic for instance in person-centred care, currently being implemented globally, where care is delivered increasingly extramurally, outside hospital walls and clinics, and instead in people’s homes, or preventatively at recreational facilities and in social contexts?  In addition to traditional quality assurance of medical devices, pharmaceuticals and clinical assessments, it can be beneficial to measure individual patient health and the added value of social support.

Specifically, this means seeking an increased stringency in measurement where human perception is a key factor. But ensuring metrological comparability (‘traceability’) and reliably declaring measurement uncertainty when assessing patient ability or increased social capital are challenging, since subjective measurements are often characterised by large dispersion; the usual tools of statistics do not always work on the categorical scales typical of such measurements; and an independent objective reality in what is being measured might be questioned.

Drawing simple analogies between ‘instruments’ in the social sciences – questionnaires, ability tests, etc.–  and  engineering instruments such as thermometers merely in terms of measurement error does not go far enough. A possible way forward apparently equally applicable to both physical and social measurement, seems to be to model inferences in terms of a measurement system , where specifically the output of the instrument in response to probing an object (‘entity’) is a performance metric, i.e., how well the set-up performs the assessment.

In physical measurements, system performance can be quantified in terms of decision risks arise from measurement uncertainty, for instance when assessing compliance to a specification limit. Typical outputs in both physical and social measurement are performance metrics, for instance on an ordinal scale 0 – 100% which is not directly suitable for metrological assurance. Using a psychometric, generalised linear model, where so-called ‘latent’ (or ‘explanatory’) variables – task challenge or person ability – are derived from the response of the measurement system, can yield quantities which seem to possess quantitative characteristics akin to those of physical quantities. Metrological references for comparability via traceability and reliable estimates of uncertainty and decision risks are then in reach even for perceptive measurements. Current EU projects, such as EMPIR HLT04 NeuroMet and EIT Health Pre-D, are attempting such metrology in the case of Alzheimer’s and Diabetes, respectively.

Once metrological assurance has been achieved, measurements in person-centred care may subsequently provide an objective basis for making decisions in Conformity Assessment to determine, directly or indirectly, whether an entity (product, process, system, person or body) meets relevant standards or fulfils specified requirements in regulated areas, such as the health sector and product safety testing. As in more traditional areas such as ensuring fair trade in commodity and retail contexts, legal metrology in healthcare can be regarded as a prototype for more general Conformity Assessment.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Quality assurance, person-centred care, legal metrology, psychometrics
National Category
Neurology
Identifiers
URN: urn:nbn:se:ri:diva-31286OAI: oai:DiVA.org:ri-31286DiVA: diva2:1144166
Conference
18th International Metrologie Congress,19-21 September 2017. Paris, France
Available from: 2017-09-25 Created: 2017-09-25 Last updated: 2017-10-02Bibliographically approved

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