Dynamic Clinical Data Mining: Search Engine-Based Decision Support

Abstract

The research world is undergoing a transformation into one in which data, on massive levels, is freely shared. In the clinical world, the capture of data on a consistent basis has only recently begun. We propose an operational vision for a digitally based care system that incorporates data-based clinical decision making. The system would aggregate individual patient electronic medical data in the course of care; query a universal, de-identified clinical database using modified search engine technology in real time; identify prior cases of sufficient similarity as to be instructive to the case at hand; and populate the individual patient's electronic medical record with pertinent decision support material such as suggested interventions and prognosis, based on prior outcomes. Every individual's course, including subsequent outcomes, would then further populate the population database to create a feedback loop to benefit the care of future patients.

An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support (Preprint)

“…A Clinical Decision Support System (CDSS) is a health information system that is integrated into Electronic Health Records (EHR), enabling easy and effective use by physicians [9]. A CDSS incorporates individual patient data, a rule engine, and a medical knowledge base to produce a patient-specific assessment or recommendation of a management plan [10]. CDSS usually rely on the processing of clinical data gathered into EHR.…”

Challenges and Opportunities in Secondary Analyses of Electronic Health Record Data

“…Perhaps this is sufficient for the clinician to recommend a new clinical practice; or maybe they will design a pragmatic observational study for more nuance-evaluating dose-responsiveness, or adverse effect profiles across subpopulations. As clinical decisions are made and the patient's course of care shaped, this intervention and outcomes information is entered into the electronic health record, effectively creating a feedback loop for future inquiries [34]. Of course, the advantages of secondary analysis of electronic health records must always be balanced with ethical considerations.…”

Pulling It All Together: Envisioning a Data-Driven, Ideal Care System

“…[2] How can the documentation process of data entry support and enhance the medical decision-making process? The first iteration of electronic health records (EHRs) has simply attempted to replicate the traditional paper documentation in a digital format.…”

“…dynamic clinical data mining, closing the loop on ICU data, optimizing the data system itself, crowdsourcing, etc., and will attempt to 'pull it all together' in this chapter, which we hope will inspire and encourage others to think about and move to create such a system [1][2][3][4][5][6][7][8][9][10]. Such a system, in theory, would support clinical workflow by [1] leveraging data to provide both accurate personalized, or 'precision,' care for individuals while ensuring optimal care at a population level; [2] providing coordination and communication among the users of the system; and [3] defining, tracking, and enhancing safety and quality. While health care is intrinsically heterogeneous at the level of individual patients, encounters, specialties, and clinical settings, we also propose some general systems-based solutions derived from contextually defined use cases.…”