Problem-oriented patient record model as a conceptual foundation for a multi-professional electronic patient record

Use of electronic health records by child primary healthcare providers in Europe

BACKGROUND

There is limited data on the use and functionality level of electronic health records (EHRs) supporting primary child health care in Europe. Our objective was to determine European primary child healthcare providers' use of EHRs, and functionality level of the systems used.

METHODS

European primary care paediatricians, paediatric subspecialists and family doctors were invited by European Academy of Paediatrics Research in Ambulatory Setting Network (EAPRASnet) country coordinators to complete a web-based survey on the use of EHRs and the systems' functionalities. Binomial logistic analysis has been used to evaluate the effect of specialty and type of practice on the use of EHRs.

RESULTS

The survey was completed by 679 child primary healthcare providers (response rate 53%). Five hundred and fifty four responses coming from 10 predominant countries were taken for further analysis. EHR use by respondents varied widely between countries, all electronic type use ranging between 7% and 97%. There was no significant difference in EHR use between group practice and solo practitioners, or between family doctors and primary care paediatricians. History and physical examination can be properly recorded by respondents in most countries. However, growth chart plotting capacity in some countries ranges between 22% and 50%. Vaccination recording capacity varies between 50% and 100%, and data exchange capacity with immunization databases is mostly limited, ranging between 0% and 54%.

CONCLUSIONS

There is marked heterogeneity in the use and functionalities of EHRs used among child primary child healthcare providers in Europe. More importantly, lack of critical paediatric supportive functionalities like growth tracking and vaccination status has been documented in some countries. There is a need to explore the reasons for these findings, and to develop a cross European paediatric EHR standards.

Health care transformation through collaboration on open-source informatics projects: integrating a medical applications platform, research data repository, and patient summarization

Background

The Strategic Health IT Advanced Research Projects (SHARP) program seeks to conquer well-understood challenges in medical informatics through breakthrough research. Two SHARP centers have found alignment in their methodological needs: (1) members of the National Center for Cognitive Informatics and Decision-making (NCCD) have developed knowledge bases to support problem-oriented summarizations of patient data, and (2) Substitutable Medical Apps, Reusable Technologies (SMART), which is a platform for reusable medical apps that can run on participating platforms connected to various electronic health records (EHR). Combining the work of these two centers will ensure wide dissemination of new methods for synthesized views of patient data. Informatics for Integrating Biology and the Bedside (i2b2) is an NIH-funded clinical research data repository platform in use at over 100 sites worldwide. By also working with a co-occurring initiative to SMART-enabling i2b2, we can confidently write one app that can be used extremely broadly.

Objective

Our goal was to facilitate development of intuitive, problem-oriented views of the patient record using NCCD knowledge bases that would run in any EHR. To do this, we developed a collaboration between the two SHARPs and an NIH center, i2b2.

Methods

First, we implemented collaborative tools to connect researchers at three institutions. Next, we developed a patient summarization app using the SMART platform and a previously validated NCCD problem-medication linkage knowledge base derived from the National Drug File-Reference Terminology (NDF-RT). Finally, to SMART-enable i2b2, we implemented two new Web service “cells” that expose the SMART application programming interface (API), and we made changes to the Web interface of i2b2 to host a “carousel” of SMART apps.

Results

We deployed our SMART-based, NDF-RT-derived patient summarization app in this SMART-i2b2 container. It displays a problem-oriented view of medications and presents a line-graph display of laboratory results.

Conclusions

This summarization app can be run in any EHR environment that either supports SMART or runs SMART-enabled i2b2. This i2b2 “clinical bridge” demonstrates a pathway for reusable app development that does not require EHR vendors to immediately adopt the SMART API. Apps can be developed in SMART and run by clinicians in the i2b2 repository, reusing clinical data extracted from EHRs. This may encourage the adoption of SMART by supporting SMART app development until EHRs adopt the platform. It also allows a new variety of clinical SMART apps, fueled by the broad aggregation of data types available in research repositories. The app (including its knowledge base) and SMART-i2b2 are open-source and freely available for download.

Problem-centric organization and visualization of patient imaging and clinical data

A patient's electronic medical record contains a large amount of unstructured textual information. As patient records become increasingly dense owing to an aging population and increased occurrence of chronic diseases, a tool is needed to help organize and navigate patient data in a way that facilitates a clinician's ability to understand this information and that improves efficiency. A system has been developed for physicians that summarizes clinical information from a patient record. This system provides a gestalt view of the patient's record by organizing information about each disease along four dimensions (axes): time (eg, disease progression over time), space (eg, tumor in left frontal lobe), existence (eg, certainty of existence of a finding), and causality (eg, response to treatment). A display is generated from information provided by radiology reports and discharge summaries. Natural language processing is used to identify clinical abnormalities (problems, symptoms, findings) from these reports as well as associated properties and relationships. This information is presented in an integrated format that organizes extracted findings into a problem list, depicts the information on a timeline grid, and provides direct access to relevant reports and images. The goal of this system is to improve the structure of clinical information and its presentation to the physician, thereby simplifying the information retrieval and knowledge discovery necessary to bridge the gap between acquiring raw data and making an informed diagnosis.