Authors:Shivers; Jennifer, Amlung, Joseph, Cullen, Theresa Abstract: Background An effective health system is dependent on the people, processes, and technology required to support its successful use. Technology, including essential health information technology (HIT) components, is not enough to successfully support quality patient care delivery. Without a strong support structure, HIT tools and data alone will fail to meet the desired goal of quality patient care that achieves health equity. Multiple factors, including governance processes, policies, and standards, are critical for the development, implementation, and consistent and effective use of clinical HIT systems and the resulting data. A comprehensive model that provided support for a holistic assessment of the HIT ecosystem could not be identified. The Facility HIT Ecosystem Capability Maturity Model (ECMM) was designed in response to this need. Objectives This project was designed to create and propose a comprehensive framework for self-assessment and support of a maturation pathway for a facility's comprehensive HIT ecosystem, including governance processes, policies, and standards. Methods Iterative methods based on agile and human-centered design practices were used to create and validate the framework and its contents. Results The ECMM and toolkit support a health care facility's clinical or technical leadership, including chief medical officers, chief information officers, and chief medical/health/nursing informatics officers, in identifying and assessing policies, processes, and capabilities surrounding HIT. This framework supports iterative evaluation and step-ladder style progression and goal setting to achieve desired capabilities, both at the local level and at the level of supporting organizations. Reviewer feedback was used to provide iterative tool improvement and refinement. Conclusion The proposed HIT maturity model toolkit showed potential as a tool to help empower health care facilities and their leadership to advance their HIT ecosystem. Citation: ACI Open 2021; : PubDate: 2020-08-12T00:00:00+01:00 DOI: 10.1055/s-0040-1715171 Issue No:Vol.
Informati, No. Informatics Governance (2020)
Authors:Rasmussen; Luke V., Hoell, Christin, Smith, Maureen E., Chisholm, Rex, Starren, Justin, Aufox, Sharon, Luo, Yuan, Rasmussen-Torvik, Laura J. Abstract: Background While there have been published reports detailing technical challenges of incorporating genetic test results into the electronic health record (EHR) with proposed solutions, less has been published about unanticipated sociotechnological or practical communication challenges involved in this process. Objectives This study was aimed to describe unanticipated issues that arose returning genetic research results through the EHR as part of the National Human Genome Research Institute (NHGRI)-funded electronic Medical Records and Genomics (eMERGE) 3 consortium, and provide lessons learned for future implementations Methods We sequenced 3,000 participants on a 109-gene panel and returned genetic results initially in person and/or by letter, with a later release directly into the EHR and patient portal. Results When results were returned through the EHR, multiple participants expressed confusion and contacted the health system, resulting in our institution temporarily freezing our return of research results. Discussion We determined the likely causes of this issue to be (1) the delay between enrollment and results return, (2) inability to personalize mass e-mail messages announcing new research test results in the EHR, (3) limited space for description of test results in the EHR, and (4) the requirement to list an ordering physician for research results in the EHR. For future return of results, we propose sending preparatory e-mails to participants, including screenshots of how they can expect to see their results presented in the EHR portal. Conclusion We hope our lessons learned can provide helpful guidance to other sites implementing research genetic results into the EHR and can encourage EHR developers to incorporate greater flexibility in the future. Citation: ACI Open 2021; : PubDate: 2020-11-13T00:00:00+0100 DOI: 10.1055/s-0040-1719059 Issue No:Vol.
Genomic D, No. Genomic Data in CIS (2020)
Authors:Prows; Cynthia A., Marsolo, Keith, Myers, Melanie F., Nix, Jeremy, Hall, Eric S. Abstract: Background We offered adolescents personalized choices about the type of genetic results they wanted to learn during a research study and created a workflow to filter and transfer the results to the electronic health record (EHR). Methods We describe adaptations needed to ensure that adolescents' results documented in the EHR and returned to adolescent/parent dyads matched their choices. A web application enabled manual modification of the underlying laboratory report data based on adolescents' choices. The final PDF format of the laboratory reports was not viewable through the EHR patient portal, so an EHR form was created to support the manual entry of discrete results that could be viewed in the portal. Results Enabling adolescents' choices about genetic results was a labor-intensive process. More than 350 hours was required for development of the application and EHR form, as well as over 50 hours of a study professional's time to enter choices into the application and EHR. Adolescents and their parents who learned genetic results through the patient portal indicated that they were satisfied with the method of return and would make their choices again if given the option. Conclusion Although future EHR upgrades are expected to enable patient portal access to PDFs, additional improvements are needed to allow the results to be partitioned and filtered based on patient preferences. Furthermore, separating these results into more discrete components will allow them to be stored separately in the EHR, supporting the use of these data in clinical decision support or artificial intelligence applications. Citation: ACI Open 2021; : PubDate: 2020-11-13T00:00:00+0100 DOI: 10.1055/s-0040-1718747 Issue No:Vol.
Genomic D, No. Genomic Data in CIS (2020)
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