SBIR-STTR Award

Empowering Physicians with Evidence-Based Decision Support for Pediatric Rheumatologic Diagnoses This project responds to a critical shortage of pediatric rheumatologists by enlisting top rheumatologists to encapsulate the diagnostic information in their field in the most advanced decision support software tool for diagnosis. The SimulConsult tool is in active clinical use worldwide by specialists in neurology and genetics. This project focuses on two key feasibility questions with which to assess SimulConsult's market potential. One is whether diagnostic performance continues to be high when a new, non-overlapping clinical area is added. The second is whether generalists and specialists are willing to adopt the tool to improve referrals and diagnosis. Aim 1 is to add rheumatology diseases to the existing diagnostic decision support tool. Pediatric rheumatology content will be added to the SimulConsult database, using SimulConsult's evidence-based, open-database approach. Using narrative resources such as textbooks and articles as inputs, diseases and findings (signs, symptoms and lab tests) will be added for the ~100 pediatric rheumatology disease, as well as ~200 conditions not already in the database that are likely to arise in the differential diagnosis. For a subset of 25 diseases this information will be further refined by experts using disease-based, finding-based and case-based editing, adding data frequently omitted from narrative material about findings in a disease but known to experts. Aim 2 is to assess the diagnostic effectiveness and efficiency, and appropriateness of referrals. The functioning of the decision support software will be measured by testing clinicians on diagnosis and workup of real cases, before and after using the diagnostic decision support. Testers will include generalists (pediatricians, family practitioners, and emergency department pediatricians) and rheumatologists, both senior and junior. Aim 3 is to assess whether modifications to the data structure are needed to deal with issues such as different granularity of information in different areas of medicine, and develop effective approaches to those needs. Current structures in SimulConsult such as "modifier findings" and "bundles" of findings will be assessed and modified as necessary to represent findings simultaneously to rheumatologists and neurogeneticists. Success plus input about what else will be needed for adoption will set the stage for a second phase to scale up detailed coverage across the remaining 275 diseases relevant to pediatric rheumatology and to make the required changes for adoption.

Public Health Relevance:
Empowering Physicians with Evidence-Based Decision Support for Pediatric Rheumatologic Diagnoses. This project responds to a critical shortage of pediatric rheumatologists by enlisting top rheumatologists to encapsulate the diagnostic information in their field in the most advanced decision support software tool for diagnosis. The project tests workability of integration into a tool used in other areas of medicine and assesses the benefits of such rheumatology assistance for both rheumatologists and general clinicians. By doing so, it tests whether this decision support approach can be used more widely to improve accuracy and cost- effectiveness in medicine.

Public Health Relevance Statement:
Empowering Physicians with Evidence-Based Decision Support for Pediatric Rheumatologic Diagnoses. This project responds to a critical shortage of pediatric rheumatologists by enlisting top rheumatologists to encapsulate the diagnostic information in their field in the most advanced decision support software tool for diagnosis. The project tests workability of integration into a tool used in other areas of medicine and assesses the benefits of such rheumatology assistance for both rheumatologists and general clinicians. By doing so, it tests whether this decision support approach can be used more widely to improve accuracy and cost- effectiveness in medicine.

NIH Spending Category:
Arthritis; Clinical Research; Pediatric

Project Terms:
Accident and Emergency department; Adopted; Adoption; Area; base; case-based; Child; Childhood; Clinical; clinical practice; Collection; Computer software; cost; cost effectiveness; Data; data structure; Databases; Diagnosis; Diagnostic; Differential Diagnosis; Disease; editorial; Effectiveness; empowered; Encapsulated; evidence base; Family; Feedback; General Practitioners; Genetic; Hereditary Disease; Human Resources; improved; Individual; Marketing; Measures; medical specialties; Medicine; Modification; neurogenetics; Neurologic; Neurology; pediatrician; Performance; Phase; Physicians; Published Comment; Resources; rheumatologist; Rheumatology; scale up; Signs and Symptoms; Software Tools; Specialist; Staging; Structure; success; System; Testing; Textbooks; Time; tool; willingness

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.

Public Health Relevance Statement:


Public Health Relevance:
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.

NIH Spending Category:
Clinical Research; Networking and Information Technology R&D; Patient Safety

Project Terms:
Address; Back; base; Breeding; Caring; Clinical; Code; Computer software; computerized physician order entry; cost; cost effectiveness; Data; Databases; Decision Support Systems; Diagnosis; Diagnostic; diagnostic accuracy; Differential Diagnosis; Disease; Documentation; Effectiveness; Electronic Health Record; evidence base; experience; Foundations; Future; Genetic screening method; Goals; Health system; Healthcare; Improve Access; improved; Institution; interoperability; Knowledge; Learning; Measures; Medical; Medicine; Modeling; Monitor; Negative Finding; Output; Patients; Phase; Process; public health relevance; Reporting; satisfaction; Signs and Symptoms; Small Business Innovation Research Grant; Speed (motion); success; System; Test Result; Testing; tool; Unified Medical Language System; usability; Vision; Work