Abstract
IntroductionCurrent health care delivery relies on complex, computer-generated risk models constructed from insurance claims and medical record data. However, these models produce inaccurate predictions of risk levels for individual patients, do not explicitly guide care, and undermine health management investments in many patients at lesser risk. Therefore, this study prospectively validates a concise patient-reported risk assessment that addresses these inadequacies of computer-generated risk models.MethodsFive measures with well-documented impacts on the use of health services are summed to create a “What Matters Index.” These measures are: 1) insufficient confidence to self-manage health problems, 2) pain, 3) bothersome emotions, 4) polypharmacy, and 5) adverse medication effects. We compare the sensitivity and predictive values of this index with two representative risk models in a population of 8619 Medicaid recipients.ResultsThe patient-reported “What Matters Index” and the conventional risk models are found to exhibit similar sensitivities and predictive values for subsequent hospital or emergency room use. The “What Matters Index” is also reliable: akin to its performance during development, for patients with index scores of 1, 2, and ≥3, the odds ratios (with 95% confidence intervals) for subsequent hospitalization within 1 year, relative to patients with a score of 0, are 1.3 (1.1–1.6), 2.0 (1.6–2.4), and 3.4 (2.9–4.0), respectively; for emergency room use, the corresponding odds ratios are 1.3 (1.1–1.4), 1.9 (1.6–2.1), and 2.9 (2.6–3.3). Similar findings were replicated among smaller populations of 1061 mostly older patients from nine private practices and 4428 Medicaid patients without chronic conditions.SummaryIn contrast to complex computer-generated risk models, the brief patient-reported “What Matters Index” immediately and unambiguously identifies fundamental, remediable needs for each patient and more sensibly directs the delivery of services to patient categories based on their risk for subsequent costly care.
Highlights
MethodsFive measures with well-documented impacts on the use of health services are summed to create a “What Matters Index.” These measures are: 1) insufficient confidence to self-manage health problems, 2) pain, 3) bothersome emotions, 4) polypharmacy, and 5) adverse medication effects
Current health care delivery relies on complex, computer-generated risk models constructed from insurance claims and medical record data
Despite this Medicaid population’s youth (40% aged 18–49 and none over 65), it has a high prevalence of serious chronic conditions such as diabetes (31%), respiratory diseases (39%), and atherosclerosis (17%), and more than a third (35%) report taking more than 5 prescription medications
Summary
Five measures with well-documented impacts on the use of health services are summed to create a “What Matters Index.” These measures are: 1) insufficient confidence to self-manage health problems, 2) pain, 3) bothersome emotions, 4) polypharmacy, and 5) adverse medication effects. Five measures with well-documented impacts on the use of health services are summed to create a “What Matters Index.”. These measures are: 1) insufficient confidence to self-manage health problems, 2) pain, 3) bothersome emotions, 4) polypharmacy, and 5) adverse medication effects. Patient members and office practices of a Midwestern statewide Medicaid program were asked to complete a comprehensive, free, online health assessment called HowsYourHealth Of the 26,130 adults who completed the survey in 2014, 8771 fulfilled the eligibility criteria for this prospective assessment, which were identical to those used to develop the WMI and were based on patient self-identification of at least one of five chronic conditions—hypertension, cardiovascular disease, diabetes, respiratory disease, or arthritis—or use of at least one chronic medication. Of the 8771 eligible Medicaid patients with one year of outcome data, 152 had incompletely responded to the WMI variables and were eliminated from the analysis
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