This proposal builds on the foundation of the Phase 1 SBIR work that demonstrated that diagnostic decision support software (DDSS) can improve accuracy of diagnosis and efficiency of workup. Phase 2, described here, brings these capabilities more into the clinical workflow through interoperability across multiple Electronic Health Records (EHRs). This will be done using the model of interoperability enunciated by Mandl and Kohane, in which deep clinical experience is provided to multiple EHRs by "best of breed" decision support software. The proposal builds on partnerships with Intermountain Healthcare, which has its own EHR, and with Geisinger Health System and others that use Epic.
The specific aims are to improve access to DDSS from the EHR, improve the DDSS itself, use the DDSS for coded documentation in the EHR, and use the DDSS to improve test ordering and medical necessity justification. The underlying theme of this work is to use the knowledge of the DDSS to compute with medical information as data, and use this data to improve the accuracy and cost-effectiveness of medicine. Data is collected in ways that prompt for information based on the deep understanding of the clinical situations of the DDSS. The information is classified by pertinence, which is made possible by the knowledge in the DDSS. The data is coded using interoperable codes passed to the EHR, allowing the data to be re-used for an evidence-based discussion among clinicians and those performing and interpreting lab tests. The data is also used for an evidence-based process of medical necessity justification. The data is also used as the basis for evidence-based curation of information to improve the DDSS itself, thus learning from clinical experience. The data is also used in the background to "lurk" in the EHR and advise use of DDSS when appropriate. Doing so using the interoperability model enunciated by Mandl and Kohane makes this information in the DDSS available to multiple EHRs. The resulting benefits of accuracy and efficiency will provide not only a platform for the success of the SimulConsult DDSS in the marketplace, but also help advance the interoperability model more generally. Doing so will facilitate the use of "best of breed" knowledge tools more widely in medicine to improve medical care and make it more affordable.
This project uses the power of diagnostic decision support software to provide advanced capabilities to multiple electronic health records. The aims are to use the deep knowledge of such a tool to improve accuracy and cost- effectiveness of medical care. More generally, the goal is to advance the vision of best of breed knowledge tools making medical care better and more affordable.