Thomas Gallagher

Using the CareMap with Health Incidents Statistics for Generating the Realistic Synthetic Electronic Healthcare Record

The de-personalised Electronic Healthcare Record (EHR) for secondary use has suffered re-identification. The Realistic Synthetic EHR (RS-EHR) is a promising solution safe from the threat of re-identification. This paper addresses the problem of generating the RS-EHR without using the real EHR by exploiting published Clinical Practice Guidelines (CPG) and Health Incidence Statistics (HIS). The CoMSER method takes a constraint-based approach involving: (1) formalising CPGs into the CareMap constraint and the CareMap into the State Transition Machine (STM), (2) incorporating published HIS-based constraints into the STM, and (3) exploiting domain expertise in verifying domain knowledge and creating the re-usable library of clinical notes. A preliminary evaluation of the CoMSER Method produces the RS-EHR that is considered realistic. The main contribution of this work is the approach that uses an HIS-enriched and CPG-based CareMap for generating RS-EHR with neither access to the real EHR nor using de-personalised EHR.

Approach and Method for Generating Realistic Synthetic Electronic Healthcare Records for Secondary Use

This position paper presents research work involving the development of a publicly available Realistic Synthetic Electronic Healthcare Record RS-EHR. The paper presents PADARSER, a novel approach in which the real Electronic Healthcare Record EHR and neither authorization nor anonymisation are required in generating the synthetic EHR data sets. The GRiSER method is presented for use in PADARSER to allow the RS-EHR to be synthesized for statistically significant localised synthetic patients with statistically prevalent medical conditions based upon information found from publicly available data sources. In treating the synthetic patient within the GRiSER method, clinical workflow or careflows Cfs are derived from Clinical Practice Guidelines CPGs and the standard local practices of clinicians. The Cfs generated are used together with health statistics, CPGs, medical coding and terminology systems to generate coded synthetic RS-EHR entries from statistically significant observations, treatments, tests, and procedures. The RS-EHR is thus populated with a complete medical history describing the resulting events from treating the medical conditions. The strength of the PADARSER approach is its use of publicly available information. The strengths of the GRiSER method are that 1 it does not require the use of the real EHR for generating the coded RS-EHR entries; and 2 the generic components for obtaining careflow from CPGs and for generating coded RS-EHR entries are applicable in other areas such as knowledge transfer and EHR user interfaces respectively.

Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record

Abstract Objective Our objective is to create a source of synthetic electronic health records that is readily available; suited to industrial, innovation, research, and educational uses; and free of legal, privacy, security, and intellectual property restrictions. Materials and Methods We developed Synthea, an open-source software package that simulates the lifespans of synthetic patients, modeling the 10 most frequent reasons for primary care encounters and the 10 chronic conditions with the highest morbidity in the United States. Results Synthea adheres to a previously developed conceptual framework, scales via open-source deployment on the Internet, and may be extended with additional disease and treatment modules developed by its user community. One million synthetic patient records are now freely available online, encoded in standard formats (eg, Health Level-7 [HL7] Fast Healthcare Interoperability Resources [FHIR] and Consolidated-Clinical Document Architecture), and accessible through an HL7 FHIR application program interface. Discussion Health care lags other industries in information technology, data exchange, and interoperability. The lack of freely distributable health records has long hindered innovation in health care. Approaches and tools are available to inexpensively generate synthetic health records at scale without accidental disclosure risk, lowering current barriers to entry for promising early-stage developments. By engaging a growing community of users, the synthetic data generated will become increasingly comprehensive, detailed, and realistic over time. Conclusion Synthetic patients can be simulated with models of disease progression and corresponding standards of care to produce risk-free realistic synthetic health care records at scale.

The ATEN Framework for Creating the Realistic Synthetic Electronic Health Record.

Realistic synthetic data are increasingly being recognized as solutions to lack of data or privacy concerns in healthcare and other domains, yet little effort has been expended in establishing a generic framework for characterizing, achieving and validating realism in Synthetic Data Generation (SDG). The objectives of this paper are to: (1) present a characterization of the concept of realism as it applies to synthetic data; and (2) present and demonstrate application of the generic ATEN Framework for achieving and validating realism for SDG. The characterization of realism is developed through insights obtained from analysis of the literature on SDG. The development of the generic methods for achieving and validating realism for synthetic data was achieved by using knowledge discovery in databases (KDD), data mining enhanced with concept analysis and identification of characteristic, and classification rules. Application of this framework is demonstrated by using the synthetic Electronic Healthcare Record (EHR) for the domain of midwifery. The knowledge discovery process improves and expedites the generation process; having a more complex and complete understanding of the knowledge required to create the synthetic data significantly reduce the number of generation iterations. The validation process shows similar efficiencies through using the knowledge discovered as the elements for assessing the generated synthetic data. Successful validation supports claims of success and resolves whether the synthetic data is a sufficient replacement for real data. The ATEN Framework supports the researcher in identifying the knowledge elements that need to be synthesized, as well as supporting claims of sufficient realism through the use of that knowledge in a structured approach to validation. When used for SDG, the ATEN Framework enables a complete analysis of source data for knowledge necessary for correct generation. The ATEN Framework ensures the researcher that the synthetic data being created is realistic enough for the replacement of real data for a given use-case.

Learning Health Systems: The research community awareness challenge

The learning health system (LHS) is one in which progress in science, informatics and care culture converges to continuously create new knowledge as a natural by-product of care processes. While LHS was first described over a decade ago, much of the recent published work that should fall within the domain of LHS fails to claim or be identified as such. This observation was confirmed through a review of papers published at the recent 2017 IEEE International Conference on Health Informatics (ICHI 2017), where no single LHS solution had been so identified. The authors lacked awareness that their work represented an LHS, or of any discrete classification for their work within the LHS domain. We believe this lack of awareness inhibits continued LHS research and prevents formation of a critical mass of researchers within the domain. Efforts to produce a framework and classification structure to enable confident identification of work with the LHS domain are urgently needed to address this pressing research community challenge.

The Heimdall Framework for Supporting Characterisation of Learning Health Systems

Background There are many proposed benefits of using learning health systems (LHSs), including improved patient outcomes. There has been little adoption of LHS in practice due to challenges and barriers that limit adoption of new data-driven technologies in healthcare. We have identified a more fundamental explanation: the majority of developments in LHS are not identified as LHS. The absence of a unifying namespace and framework brings a lack of consistency in how LHS is identified and classified. As a result, the LHS ‘community’ is fragmented, with groups working on similar systems being unaware of each other’s work. This leads to duplication and the lack of a critical mass of researchers necessary to address barriers to adoption. Objective To find a way to support easy identification and classification of research works within the domain of LHS. Method A qualitative meta-narrative study focusing on works that self-identified as LHS was used for two purposes. First, to find existing standard definitions and frameworks using these to create a new unifying framework. Second, seeking whether it was possible to classify those LHS solutions within the new framework. Results The study found that with apparently limited awareness, all current LHS works fall within nine primary archetypes. These findings were used to develop a unifying framework for LHS to classify works as LHS, and reduce diversity and fragmentation within the domain. Conclusions Our finding brings clarification where there has been limited awareness for LHS among researchers. We believe our framework is simple and may help researchers to classify works in the LHS domain. This framework may enable realisation of the critical mass necessary to bring more substantial collaboration and funding to LHS. Ongoing research will seek to establish the framework’s effect on the LHS domain.