Development of a care guidance index based on what matters to patients

Abstract

Introduction

Targeting resources for a designated higher-risk subgroup is a strategy for chronic care management. However, risk-designation has several limitations: it is inaccurate, seldom helpful for care guidance, and potentially misallocates care away from many patients.

Methods

To address limitations of risk designation, we tested a “what matters index” (WMI) in 19,593 adult patients with chronic conditions. The WMI contains five binary measures: insufficient confidence to manage health problems, level of pain, emotional problems, polypharmacy, and adverse medication effects. We examined its sum for association with patient-reported quality of life and prior emergency or hospital use. We compared its accuracy to a prototypic risk-designation model.

Results

The WMI was a good indicator for quality of life and in three diverse test populations it was strongly associated with the use of hospital and emergency services. For example, a sum of WMI ≥2 was associated with twice as many average uses as none; for ≥3, uses were three times higher. However, since relatively few patients use costly care, both the WMI and a prototypic risk-designation model had comparably low-positive predictive values.

Summary

The WMI uses the patient voice to identify needs strongly associated with quality of life. Akin to risk designation models, the WMI can be used to place patients into groups associated with levels of costly services, but neither is likely to forecast costly service use for individuals. However, unlike risk-designation models, the WMI is based on measures that will immediately guide care for every patient.

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Correspondence to John H. Wasson.

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Wasson, J.H., Soloway, L., Moore, L.G. et al. Development of a care guidance index based on what matters to patients. Qual Life Res 27, 51–58 (2018). https://doi.org/10.1007/s11136-017-1573-x

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Keywords

  • Patient-reported measures
  • Chronic condition management
  • Patient engagement
  • Risk models
  • Clinical prediction rules
  • Health confidence