Population health management of low-density lipoprotein cholesterol via a remote, algorithmic, navigator-executed program.

BACKGROUND: Implementation of guideline-directed cholesterol management remains low despite definitive evidence establishing such measures reduce cardiovascular (CV) events, especially in high atherosclerotic CV disease (ASCVD) risk patients. Modern electronic resources now exist that may help improve health care delivery. While electronic medical records (EMR) allow for population health screening, the potential for coupling EMR screening to remotely delivered algorithmic population-based management has been less studied as a way of overcoming barriers to optimal cholesterol management. METHODS: In an academically affiliated healthcare system, using EMR screening, we sought to identify 1,000 high ASCVD risk patients not meeting guideline-directed low-density lipoprotein-cholesterol (LDL-C) goals within specific system-affiliated primary care practices. Contacted patients received cholesterol education and were offered a remote, guideline-directed, algorithmic cholesterol management program executed by trained but non-licensed "navigators" under professional supervision. Navigators used telephone, proprietary software and internet resources to facilitate algorithm-driven, guideline-based medication initiation/titration, and laboratory testing until patients achieved LDL-C goals or exited the program. As a clinical effectiveness program for cholesterol guideline implementation, comparison was made to those contacted patients who declined program-based medication management, and received education only, along with their usual care. RESULTS: 1021 patients falling into guideline-defined high ASCVD risk groups warranting statin therapy (ASCVD, type 2 diabetes, LDL ≥ 190 mg/dL, calculated 10-year ASCVD risk ≥7.5%) and not achieving guideline-defined target LDL-C levels and/or therapy were identified and contacted. Among the 698 such patients who opted for program medication management, significant LDL-C reductions occurred in the total cohort (mean -65.4 mg/dL, 45% decrease), and each high ASCVD risk subgroup: ASCVD (-57.2 mg/dL, -48.0%); diabetes mellitus (-53.1 mg/dL, -40.0%); severe hypercholesterolemia (-76.3 mg/dL, -45.7%); elevated ASCVD 10-year risk (-62.8 mg/dL, -41.1%) (P<0.001 for all), without any significant complications. Among 20% of participants with reported statin intolerance, average LDL-C decreased from baseline 143 mg/dL to 85 mg/dL using mainly statins and ezetimibe, with limited PCSK9 inhibitor use. In comparison, eligible high ASCVD risk patients who were contacted but opted for education only, a 17% LDL-C decrease occurred over a similar timeframe, with 80% remaining with an LDL-C over 100 mg/dL. CONCLUSIONS: A remote, algorithm-driven, navigator-executed cholesterol management program successfully identified high ASCVD risk undertreated patients using EMR screening and was associated with significantly improved guideline-directed LDL-C control, supporting this approach as a novel strategy for improving health care access and delivery.

Extending i2b2 into a framework for semantic abstraction of EHR to facilitate rapid development and portability of Health IT applications.

The wide gap between a care provider's conceptualization of electronic health record (EHR) and the structures for electronic health record (EHR) data storage and transmission, presents a multitude of obstacles for development of innovative Health IT applications. While developers model the EHR view of the clinicians at one end, they work with a different data view to construct health IT applications. Although there has been considerable progress to bridge this gap by evolution of developer friendly standards and tools for terminology mapping and data warehousing, there is a need for a simplified framework to facilitate development of interoperable applications. To this end, we propose a framework for creating a layer of semantic abstraction on the EHR and describe preliminary work on the implementation of this framework for management of hyperlipidemia and hypertension. Our goal is to facilitate the rapid development and portability of Health IT applications.