Persistent Cardiometabolic Health Gaps: Can Therapeutic Care Gaps Be Precisely Identified from Electronic Health Records

Abstract

The objective of this study was to determine the strengths and limitations of using structured electronic health records (EHR) to identify and manage cardiometabolic (CM) health gaps. We used medication adherence measures derived from dispense data to attribute related therapeutic care gaps (i.e., no action to close health gaps) to patient- (i.e., failure to retrieve medication or low adherence) or clinician-related (i.e., failure to initiate/titrate medication) behavior. We illustrated how such data can be used to manage health and care gaps for blood pressure (BP), low-density lipoprotein cholesterol (LDL-C), and HbA1c for 240,582 Sutter Health primary care patients. Prevalence of health gaps was 44% for patients with hypertension, 33% with hyperlipidemia, and 57% with diabetes. Failure to retrieve medication was common; this patient-related care gap was highly associated with health gaps (odds ratios (OR): 1.23–1.76). Clinician-related therapeutic care gaps were common (16% for hypertension, and 40% and 27% for hyperlipidemia and diabetes, respectively), and strongly related to health gaps for hyperlipidemia (OR = 5.8; 95% CI: 5.6–6.0) and diabetes (OR = 5.7; 95% CI: 5.4–6.0). Additionally, a substantial minority of care gaps (9% to 21%) were uncertain, meaning we lacked evidence to attribute the gap to either patients or clinicians, hindering efforts to close the gaps.

Medication non-adherence and therapeutic inertia independently contribute to poor disease control for cardiometabolic diseases

“…Sutter patients > 35 years old and diagnosed with at least one cardiometabolic condition were eligible for this study. Details on the diagnosis criteria for each cardiometabolic condition are described elsewhere 34 , and EHR data back to 2010 were used to check the earliest diagnosis for each cardiometabolic condition.…”

“…The detailed criteria for defining health gaps are shown in Table 1 . Two elevated measures at two consecutive clinical encounters were required for BP, and one was required for HbA1c and LDL-C 34 . The index date for each biometric measure was defined as the date when the first elevated measure was observed within the health gap assessment period.…”

“…The index date for each biometric measure was defined as the date when the first elevated measure was observed within the health gap assessment period. TI was identified within the 6-month period after the index date, and the approach to using EHR data to identify TI was described elsewhere 34 In this study, the operational criteria for TI are found in Table 1 . In our previously published work, the study period ended at 9/30/2017; to study persistence of care gaps, in this study we followed patients until 12/31/2020 (See Fig.…”

“…However, adherence to cardiometabolic-related medications is still suboptimal and varies significantly by medication type and underlying conditions 24 – 31 with the lowest adherence in statins (25–40%) 32 . Most previous studies focus on assessing the impact of a single component on disease control, whereas studies that simultaneously study both TI and adherence are still lacking 34 . Readily available medication dispense data in electronic health records (EHRs) makes it possible to derive measures of medication adherence to assess how much these factors contribute to biometric measure control.…”

“…However, few studies report the time to achieve disease control in real-world practice. Among adult patients with a cardiometabolic condition, more than half had two or more cardiometabolic diseases 34 , 39 , denoted as multimorbidity, implying a potential common underlying pathophysiological mechanism. The interplay of risk factors for cardiovascular disease suggests that cardiometabolic conditions and risk factors should be managed together 40 , 41 .…”