I-Climate: A “Clinical Climate Informatics” Action Framework to Reduce Environmental Pollution From Healthcare

Addressing environmental pollution and climate change is one of the biggest sociotechnical challenges of our time. While information technology has led to improvements in healthcare, it has also contributed to increased energy usage, destructive natural resource extraction, piles of e-waste, and increased greenhouse gases. We introduce a framework Information technology-enabled Clinical cLimate InforMAtics acTions for the Environment (i-CLIMATE) to illustrate how clinical informatics can help reduce healthcare\u27s environmental pollution and climate-related impacts using 5 actionable components: (1) create a circular economy for health IT, (2) reduce energy consumption through smarter use of health IT, (3) support more environmentally friendly decision-making by clinicians and health administrators, (4) mobilize healthcare workforce environmental stewardship through informatics, and (5) Inform policies and regulations for change. We define Clinical Climate Informatics as a field that applies data, information, and knowledge management principles to operationalize components of the i-CLIMATE Framework

Comparison of clinical knowledge management capabilities of commercially-available and leading internally-developed electronic health records

<p>Abstract</p> <p>Background</p> <p>We have carried out an extensive qualitative research program focused on the barriers and facilitators to successful adoption and use of various features of advanced, state-of-the-art electronic health records (EHRs) within large, academic, teaching facilities with long-standing EHR research and development programs. We have recently begun investigating smaller, community hospitals and out-patient clinics that rely on commercially-available EHRs. We sought to assess whether the current generation of commercially-available EHRs are capable of providing the clinical knowledge management features, functions, tools, and techniques required to deliver and maintain the clinical decision support (CDS) interventions required to support the recently defined "meaningful use" criteria.</p> <p>Methods</p> <p>We developed and fielded a 17-question survey to representatives from nine commercially available EHR vendors and four leading internally developed EHRs. The first part of the survey asked basic questions about the vendor's EHR. The second part asked specifically about the CDS-related system tools and capabilities that each vendor provides. The final section asked about clinical content.</p> <p>Results</p> <p>All of the vendors and institutions have multiple modules capable of providing clinical decision support interventions to clinicians. The majority of the systems were capable of performing almost all of the key knowledge management functions we identified.</p> <p>Conclusion</p> <p>If these well-designed commercially-available systems are coupled with the other key socio-technical concepts required for safe and effective EHR implementation and use, and organizations have access to implementable clinical knowledge, we expect that the transformation of the healthcare enterprise that so many have predicted, is achievable using commercially-available, state-of-the-art EHRs.</p

The state of the art in clinical knowledge management: An inventory of tools and techniques

Purpose To explore the need for, and use of, high-quality, collaborative, clinical knowledge management (CKM) tools and techniques to manage clinical decision support (CDS) content. Methods In order to better understand the current state of the art in CKM, we developed a survey of potential CKM tools and techniques. We conducted an exploratory study by querying a convenience sample of respondents about their use of specific practices in CKM. Results The following tools and techniques should be priorities in organizations interested in developing successful computer-based provider order entry (CPOE) and CDS implementations: (1) a multidisciplinary team responsible for creating and maintaining the clinical content; (2) an external organizational repository of clinical content with web-based viewer that allows anyone in the organization to review it; (3) an online, collaborative, interactive, Internet-based tool to facilitate content development; (4) an enterprise-wide tool to maintain the controlled clinical terminology concepts. Even organizations that have been successfully using computer-based provider order entry with advanced clinical decision support features for well over 15 years are not using all of the CKM tools or practices that we identified. Conclusions If we are to further stimulate progress in the area of clinical decision support, we must continue to develop and refine our understanding and use of advanced CKM capabilities

Using Ai-Generated Suggestions From ChatGPT to Optimize Clinical Decision Support

OBJECTIVE: To determine if ChatGPT can generate useful suggestions for improving clinical decision support (CDS) logic and to assess noninferiority compared to human-generated suggestions. METHODS: We supplied summaries of CDS logic to ChatGPT, an artificial intelligence (AI) tool for question answering that uses a large language model, and asked it to generate suggestions. We asked human clinician reviewers to review the AI-generated suggestions as well as human-generated suggestions for improving the same CDS alerts, and rate the suggestions for their usefulness, acceptance, relevance, understanding, workflow, bias, inversion, and redundancy. RESULTS: Five clinicians analyzed 36 AI-generated suggestions and 29 human-generated suggestions for 7 alerts. Of the 20 suggestions that scored highest in the survey, 9 were generated by ChatGPT. The suggestions generated by AI were found to offer unique perspectives and were evaluated as highly understandable and relevant, with moderate usefulness, low acceptance, bias, inversion, redundancy. CONCLUSION: AI-generated suggestions could be an important complementary part of optimizing CDS alerts, can identify potential improvements to alert logic and support their implementation, and may even be able to assist experts in formulating their own suggestions for CDS improvement. ChatGPT shows great potential for using large language models and reinforcement learning from human feedback to improve CDS alert logic and potentially other medical areas involving complex, clinical logic, a key step in the development of an advanced learning health system

Leveraging Explainable Artificial Intelligence to Optimize Clinical Decision Support

OBJECTIVE: To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches. METHODS: We extracted data on alerts generated from January 1, 2019 to December 31, 2020, at Vanderbilt University Medical Center. We developed machine learning models to predict user responses to alerts. We applied XAI techniques to generate global explanations and local explanations. We evaluated the generated suggestions by comparing with alert\u27s historical change logs and stakeholder interviews. Suggestions that either matched (or partially matched) changes already made to the alert or were considered clinically correct were classified as helpful. RESULTS: The final dataset included 2 991 823 firings with 2689 features. Among the 5 machine learning models, the LightGBM model achieved the highest Area under the ROC Curve: 0.919 [0.918, 0.920]. We identified 96 helpful suggestions. A total of 278 807 firings (9.3%) could have been eliminated. Some of the suggestions also revealed workflow and education issues. CONCLUSION: We developed a data-driven process to generate suggestions for improving alert criteria using XAI techniques. Our approach could identify improvements regarding clinical decision support (CDS) that might be overlooked or delayed in manual reviews. It also unveils a secondary purpose for the XAI: to improve quality by discovering scenarios where CDS alerts are not accepted due to workflow, education, or staffing issues

The impact of electronic records on patient safety : a qualitative study

BACKGROUND: Our aim was to explore NHS staff perceptions and experiences of the impact on patient safety of introducing a maternity system. METHODS: Qualitative semi-structured interviews were conducted with 19 members of NHS staff who represented a variety of staff groups (doctors, midwives, health care assistants), staff grades (consultant and midwife grades) and wards within a maternity unit. Participants represented a single maternity unit at a NHS teaching hospital in the North of England. Interviews were conducted during the first 12 months of the system being implemented and were analysed thematically. RESULTS: Participants perceived there to be an elevated risk to patient safety during the system's implementation. The perceived risks were attributed to a range of social and technical factors. For example, poor system design and human error which resulted in an increased potential for missing information and inputting error. CONCLUSIONS: The first 12 months of introducing the maternity system was perceived to and in some cases had already caused actual risk to patient safety. Trusts throughout the NHS are facing increasing pressure to become paperless and should be aware of the potential adverse impacts on patient safety that can occur when introducing electronic systems. Given the potential for increased risk identified, recommendations for further research and for NHS trusts introducing electronic systems are proposed

Towards successful coordination of electronic health record based-referrals: a qualitative analysis

<p>Abstract</p> <p>Background</p> <p>Successful subspecialty referrals require considerable coordination and interactive communication among the primary care provider (PCP), the subspecialist, and the patient, which may be challenging in the outpatient setting. Even when referrals are facilitated by electronic health records (EHRs) (<it>i.e</it>., e-referrals), lapses in patient follow-up might occur. Although compelling reasons exist why referral coordination <it>should </it>be improved, little is known about which elements of the complex referral coordination process should be targeted for improvement. Using Okhuysen & Bechky's coordination framework, this paper aims to understand the barriers, facilitators, and suggestions for improving communication and coordination of EHR-based referrals in an integrated healthcare system.</p> <p>Methods</p> <p>We conducted a qualitative study to understand coordination breakdowns related to e-referrals in an integrated healthcare system and examined work-system factors that affect the timely receipt of subspecialty care. We conducted interviews with seven subject matter experts and six focus groups with a total of 30 PCPs and subspecialists at two tertiary care Department of Veterans Affairs (VA) medical centers. Using techniques from grounded theory and content analysis, we identified organizational themes that affected the referral process.</p> <p>Results</p> <p>Four themes emerged: lack of an institutional referral policy, lack of standardization in certain referral procedures, ambiguity in roles and responsibilities, and inadequate resources to adapt and respond to referral requests effectively. Marked differences in PCPs' and subspecialists' communication styles and individual mental models of the referral processes likely precluded the development of a <it>shared </it>mental model to facilitate coordination and successful referral completion. Notably, very few barriers related to the EHR were reported.</p> <p>Conclusions</p> <p>Despite facilitating information transfer between PCPs and subspecialists, e-referrals remain prone to coordination breakdowns. Clear referral policies, well-defined roles and responsibilities for key personnel, standardized procedures and communication protocols, and adequate human resources must be in place before implementing an EHR to facilitate referrals.</p

Recommended practices for computerized clinical decision support and knowledge management in community settings: a qualitative study

<p>Abstract</p> <p>Background</p> <p>The purpose of this study was to identify recommended practices for computerized clinical decision support (CDS) development and implementation and for knowledge management (KM) processes in ambulatory clinics and community hospitals using commercial or locally developed systems in the U.S.</p> <p>Methods</p> <p>Guided by the Multiple Perspectives Framework, the authors conducted ethnographic field studies at two community hospitals and five ambulatory clinic organizations across the U.S. Using a Rapid Assessment Process, a multidisciplinary research team: gathered preliminary assessment data; conducted on-site interviews, observations, and field surveys; analyzed data using both template and grounded methods; and developed universal themes. A panel of experts produced recommended practices.</p> <p>Results</p> <p>The team identified ten themes related to CDS and KM. These include: 1) workflow; 2) knowledge management; 3) data as a foundation for CDS; 4) user computer interaction; 5) measurement and metrics; 6) governance; 7) translation for collaboration; 8) the meaning of CDS; 9) roles of special, essential people; and 10) communication, training, and support. Experts developed recommendations about each theme. The original Multiple Perspectives framework was modified to make explicit a new theoretical construct, that of Translational Interaction.</p> <p>Conclusions</p> <p>These ten themes represent areas that need attention if a clinic or community hospital plans to implement and successfully utilize CDS. In addition, they have implications for workforce education, research, and national-level policy development. The Translational Interaction construct could guide future applied informatics research endeavors.</p