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

Bosl W, Mandel J, Jonikas M, Ramoni RB, Kohane IS, Mandl KD - Interact J Med Res (2013)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Children's Hospital Informatics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States. william.bosl@childrens.harvard.edu.

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.

No MeSH data available.


The SMART platform allows data to be accessed from any SMART-enabled electronic health record. The modular design illustrated here shows components in the MPR Monitor app. Each of the components attached to the core app may be modified without affecting the others. Additional components, such as new displays, may be added independently of the others. Actual file names from the application code are shown in blue letters in the figure.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC3815431&req=5

figure1: The SMART platform allows data to be accessed from any SMART-enabled electronic health record. The modular design illustrated here shows components in the MPR Monitor app. Each of the components attached to the core app may be modified without affecting the others. Additional components, such as new displays, may be added independently of the others. Actual file names from the application code are shown in blue letters in the figure.

Mentions: The SMART Medication Possession Ratio (MPR) Monitor app implements a recently published medication adherence prediction algorithm [7], which can help identify poor adherers early. The app presents an interface to a physician with information about individual patients. It was designed to access patient prescription fulfillment information from a SMART-enabled EHR, which is currently represented by the SMART reference EHR [8]. Patient data obtained from the EHR is used as input to an adherence prescription algorithm to determine whether a patient is likely to be non-adherent after one year, based on data obtained within the first 60 to 120 days following the first prescription fill. The algorithm used to predict the likelihood of future adherence was derived independently of the MPR Monitor app by statistical modeling with data from a large national insurance prescription fulfillment dataset. In addition to one-year non-adherence predictions, other clinically useful data is computed and displayed. This includes a gap-check algorithm that determines if gaps greater than 30 days have occurred at any time during the prescription for each medication. The organization of the app is illustrated in Figure 1. The figure shows file names from the application code in blue letters in the figure to illustrate the modularity and also as a visual guide for those who would use this as an example for writing a SMART app. The files in the “Core SMART App” box are standard required files for any Django application. SMART-specific code is embedded in these. Codes for numerical simulation are pure python calculations and have no references to SMART or Django. The graphical displays are html and JavaScript, as needed by standard Django programs.


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

Bosl W, Mandel J, Jonikas M, Ramoni RB, Kohane IS, Mandl KD - Interact J Med Res (2013)

The SMART platform allows data to be accessed from any SMART-enabled electronic health record. The modular design illustrated here shows components in the MPR Monitor app. Each of the components attached to the core app may be modified without affecting the others. Additional components, such as new displays, may be added independently of the others. Actual file names from the application code are shown in blue letters in the figure.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3815431&req=5

figure1: The SMART platform allows data to be accessed from any SMART-enabled electronic health record. The modular design illustrated here shows components in the MPR Monitor app. Each of the components attached to the core app may be modified without affecting the others. Additional components, such as new displays, may be added independently of the others. Actual file names from the application code are shown in blue letters in the figure.
Mentions: The SMART Medication Possession Ratio (MPR) Monitor app implements a recently published medication adherence prediction algorithm [7], which can help identify poor adherers early. The app presents an interface to a physician with information about individual patients. It was designed to access patient prescription fulfillment information from a SMART-enabled EHR, which is currently represented by the SMART reference EHR [8]. Patient data obtained from the EHR is used as input to an adherence prescription algorithm to determine whether a patient is likely to be non-adherent after one year, based on data obtained within the first 60 to 120 days following the first prescription fill. The algorithm used to predict the likelihood of future adherence was derived independently of the MPR Monitor app by statistical modeling with data from a large national insurance prescription fulfillment dataset. In addition to one-year non-adherence predictions, other clinically useful data is computed and displayed. This includes a gap-check algorithm that determines if gaps greater than 30 days have occurred at any time during the prescription for each medication. The organization of the app is illustrated in Figure 1. The figure shows file names from the application code in blue letters in the figure to illustrate the modularity and also as a visual guide for those who would use this as an example for writing a SMART app. The files in the “Core SMART App” box are standard required files for any Django application. SMART-specific code is embedded in these. Codes for numerical simulation are pure python calculations and have no references to SMART or Django. The graphical displays are html and JavaScript, as needed by standard Django programs.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Children's Hospital Informatics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States. william.bosl@childrens.harvard.edu.

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.

No MeSH data available.