Scalable decision support at the point of care: A substitutable electronic health record app for monitoring medication adherence

William Bosl, Joshua Mandel, Magdalena Jonikas, Rachel Badovinac Ramoni, Isaac S. Kohane, Kenneth D. Mandl

Research output: Contribution to journalArticle

Abstract

Background: Non-adherence to prescribed medications is a serious health problem in the United States, costing an estimated $100 billion per year. While poor adherence should be addressable with point of care health information technology, integrating new solutions with existing electronic health records (EHR) systems require customization within each organization, which is difficult because of the monolithic software design of most EHR products. Objective: The objective of this study was to create a published algorithm for predicting medication adherence problems easily accessible at the point of care through a Web application that runs on the Substitutable Medical Apps, Reusuable Technologies (SMART) platform. The SMART platform is an emerging framework that enables EHR systems to behave as "iPhone like platforms" by exhibiting an application programming interface for easy addition and deletion of third party apps. The app is presented as a point of care solution to monitoring medication adherence as well as a sufficiently general, modular application that may serve as an example and template for other SMART apps. Methods: The widely used, open source Django framework was used together with the SMART platform to create the interoperable components of this app. Django uses Python as its core programming language. This allows statistical and mathematical modules to be created from a large array of Python numerical libraries and assembled together with the core app to create flexible and sophisticated EHR functionality. Algorithms that predict individual adherence are derived from a retrospective study of dispensed medication claims from a large private insurance plan. Patients' prescription fill information is accessed through the SMART framework and the embedded algorithms compute adherence information, including predicted adherence one year after the first prescription fill. Open source graphing software is used to display patient medication information and the results of statistical prediction of future adherence on a clinician-facing Web interface. Results: The user interface allows the physician to quickly review all medications in a patient record for potential non-adherence problems. A gap-check and current medication possession ratio (MPR) threshold test are applied to all medications in the record to test for current non-adherence. Predictions of 1-year non-adherence are made for certain drug classes for which external data was available. Information is presented graphically to indicate present non-adherence, or predicted non-adherence at one year, based on early prescription fulfillment patterns. The MPR Monitor app is installed in the SMART reference container as the "MPR Monitor", where it is publically available for use and testing. MPR is an acronym for Medication Possession Ratio, a commonly used measure of adherence to a prescribed medication regime. This app may be used as an example for creating additional functionality by replacing statistical and display algorithms with new code in a cycle of rapid prototyping and implementation or as a framework for a new SMART app. Conclusions: The MPR Monitor app is a useful pilot project for monitoring medication adherence. It also provides an example that integrates several open source software components, including the Python-based Django Web framework and python-based graphics, to build a SMART app that allows complex decision support methods to be encapsulated to enhance EHR functionality.

Original languageEnglish (US)
Article numbere13
JournalJournal of Medical Internet Research
Volume15
Issue number7
DOIs
StatePublished - Jul 2013

Fingerprint

Point-of-Care Systems
Medication Adherence
Electronic Health Records
Boidae
Technology
Prescriptions
Software
Programming Languages
Software Design
Medical Informatics
Insurance
Libraries
Retrospective Studies
Organizations
Physicians

Keywords

  • Accountable care organizations
  • Electronic health record
  • Hospital information systems
  • Medical informatics applications
  • Medication adherence
  • Personal electronic health record

ASJC Scopus subject areas

  • Health Informatics

Cite this

Scalable decision support at the point of care : A substitutable electronic health record app for monitoring medication adherence. / Bosl, William; Mandel, Joshua; Jonikas, Magdalena; Ramoni, Rachel Badovinac; Kohane, Isaac S.; Mandl, Kenneth D.

In: Journal of Medical Internet Research, Vol. 15, No. 7, e13, 07.2013.

Research output: Contribution to journalArticle

Bosl, William ; Mandel, Joshua ; Jonikas, Magdalena ; Ramoni, Rachel Badovinac ; Kohane, Isaac S. ; Mandl, Kenneth D. / Scalable decision support at the point of care : A substitutable electronic health record app for monitoring medication adherence. In: Journal of Medical Internet Research. 2013 ; Vol. 15, No. 7.
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