Preprocessing of Physician Notes by LLMs Improves Clinical Concept Extraction Without Information Loss

ObjectivesThe Clinical Decision Support System (CDSS), which analyzes and uses electronic health records (EHR) for medical care, pursues patient-centered medical care. It is necessary to establish the CDSS in Korean medical services for objectification and standardization. For this purpose, analyses were performed on the points to be followed for CDSS implementation with a focus on herbal medicine prescription.MethodsTo establish the CDSS in the prescription of Traditional Korean Medicine, the current prescription practices of Traditional Korean Medicine doctors were analyzed. We also analyzed whether the prescription support function of the electronic chart was implemented. A questionnaire survey was conducted querying Traditional Korean Medicine doctors working at Traditional Korean Medicine clinics and hospitals, to investigate their desired CDSS functions, and their perceived effects on herbal medicine prescription. The implementation of the CDSS among the audit software developers used by the Korean medical doctors was examined.ResultsOn average, 41.2% of Traditional Korean Medicine doctors working in Traditional Korean Medicine clinics manipulated 1 to 4 herbs, and 31.2% adjusted 4 to 7 herbs. On average, 52.5% of Traditional Korean Medicine doctors working in Traditional Korean Medicine hospitals adjusted 1 to 4 herbs, and 35.5% adjusted 4 to 7 herbs. Questioning the desired prescription support function in the electronic medical record system, the Traditional Korean Medicine doctors working at Korean medicine clinics desired information on ‘medicine name, meridian entry, flavor of medicinals, nature of medicinals, efficacy,’ ‘herb combination information’ and ‘search engine by efficacy of prescription.’ The doctors also desired compounding contraindications (eighteen antagonisms, nineteen incompatibilities) and other contraindicatory prescriptions, ‘medicine information’ and ‘prescription analysis information through basic constitution analyses.’ The implementation of prescription support function varied by clinics and hospitals.ConclusionIn order to implement and utilize the CDSS in a medical service, clinical information must be generated and managed in a standardized form. For this purpose, standardization of terminology, coding of prescriptions using a combination of herbal medicines, and unification such as the preparation method and the weights and measures should be integrated.

Clinical Decision Support Tools

SummaryObjectives: Objective: Precision medicine uses individualized patient data, including genomic and social determinants of health SDoH), to provide optimized personalized patient treatment. In this scoping review, we summarize studies published in the last two years that reported on implementation of precision medicine in clinical decision support (CDS) related to precision medicine.Methods: We searched PubMed for manuscripts published in 2022 and 2023 to retrieve publications that included CDS and precision medicine keywords and Mesh terms. We reviewed the abstracts and full texts to apply the inclusion criteria that the study must have described the implementation of precision medicine related CDS within electronic health records. We extracted the domain, type of data used in CDS, target population included in the implementation from the final set of included manuscripts.Results: Our search retrieved 285 manuscripts and papers. Sixteen (16) papers met inclusion criteria after manual review of the full text. Eight of the reviewed papers studied the successful implementation of pharmacogenomics in CDS, four studies investigated the implementation of disease risk, and only one paper described the implementation of CDS integrating social determinants of health.Conclusion: Our scoping review of recent literature highlighted several findings. Pharmacogenomics is the most implemented precision medicine intervention based on published studies. Few reports describing disease risk and polygenic risk scores were found and no study addressed CDS for continuous biometric monitoring. Despite the increasing attention to social determinants of health as a key predictor of health outcomes, only one CDS incorporating SDoH have been publicly reported. Regular updates to scoping reviews can investigate barriers to implementation and identify solutions.

Identifying children ready for transfer out of the pediatric intensive care unit (PICU) is an area that may benefit from clinical decision support (CDS). We previously implemented a quality improvement (QI) initiative to accelerate the transfer evaluation of non-medically complex PICU patients with viral bronchiolitis receiving floor-appropriate respiratory support. Design a CDS tool adaptation of this QI initiative to further accelerate transfer evaluation of appropriate patients. The original initiative focused on identifying for transfer evaluation otherwise healthy children admitted to the PICU with bronchiolitis who had been receiving floor-appropriate levels of respiratory support for at least 6 hours. However, this initiative required that clinicians manually track the respiratory support of qualifying patients. We designed an electronic health record (EHR)-based CDS tool to automate identification of transfer-ready candidates. The tool parses EHR data to identify children meeting prior QI initiative criteria and alerts clinicians to assess transfer readiness once a child has been receiving floor-appropriate respiratory support for 6 hours. We compared time from reaching floor-appropriate support to placement of the transfer order ("time-to-transfer"), PICU length of stay (LOS), and hospital LOS between patients admitted prior to our QI initiative (December 1, 2018-October 19, 2019, "pre-QI phase"), during the initiative but before CDS tool implementation (October 20, 2019-February 7, 2022, "QI phase"), and after CDS implementation (February 8-November 11, 2022, "CDS phase"). CDS-phase patients (n = 131) had a shorter median time-to-transfer of 5.23 (interquartile range [IQR], 3.38-10.0) hours compared with QI-phase patients (n = 304) at 5.93 (IQR, 4.23-12.2) hours (p = 0.04). PICU and hospital LOS values decreased from the pre-QI (n = 150) to QI phase. Though LOS reductions were sustained during the CDS phase, further reductions from QI to CDS phase were not statistically significant. An EHR-based CDS adaptation of a prior QI initiative facilitated timely identification of PICU patients with bronchiolitis ready for transfer evaluation. Such tools might allow PICU clinicians to focus on other high-acuity tasks while accelerating transfer evaluation of appropriate patients.

Implementing pediatric-focused clinical decision support (CDS) into hospital electronic health records can lead to improvements in patient care and accelerate quality improvement and research initiatives. However, its design, development, and implementation can be a time-consuming and costly endeavor that may not be feasible for all hospital settings. In this cross-sectional study, we surveyed Pediatric Research in Inpatient Settings (PRĪS) Network hospitals about the availability of CDS tools to gain an understanding of the functionality available across 8 common inpatient pediatric diagnoses. Among the conditions, asthma had the most extensive CDS availability, while mood disorders had the least. Overall, freestanding children's hospitals had the greatest breadth in CDS coverage across conditions and depth in CDS types within conditions. Future initiatives should examine the relationship between CDS availability and clinical outcomes as well as its relationship with hospitals' performance executing multicenter informatics projects, quality improvement collaboratives, and implementation science strategies.

Multimorbidity, the presence of more than one condition in a single individual, is a global health issue in primary care. Multimorbid patients tend to have a poor quality of life and suffer from a complicated care process. Clinical decision support systems (CDSSs) and telemedicine are the common information and communication technologies that have been used to reduce the complexity of patient management. However, each element of telemedicine and CDSSs is often examined separately and with great variability. Telemedicine has been used for simple patient education as well as more complex consultations and case management. For CDSSs, there is variability in data inputs, intended users, and outputs. Thus, there are several gaps in knowledge about how to integrate CDSSs into telemedicine and to what extent these integrated technological interventions can help improve patient outcomes for those with multimorbidity. Our aims were to (1) broadly review system designs for CDSSs that have been integrated into each function of telemedicine for multimorbid patients in primary care, (2) summarize the effectiveness of the interventions, and (3) identify gaps in the literature. An online search for literature was conducted up to November 2021 on PubMed, Embase, CINAHL, and Cochrane. Searching from the reference lists was done to find additional potential studies. The eligibility criterion was that the study focused on the use of CDSSs in telemedicine for patients with multimorbidity in primary care. The system design for the CDSS was extracted based on its software and hardware, source of input, input, tasks, output, and users. Each component was grouped by telemedicine functions: telemonitoring, teleconsultation, tele-case management, and tele-education. Seven experimental studies were included in this review: 3 randomized controlled trials (RCTs) and 4 non-RCTs. The interventions were designed to manage patients with diabetes mellitus, hypertension, polypharmacy, and gestational diabetes mellitus. CDSSs can be used for various telemedicine functions: telemonitoring (eg, feedback), teleconsultation (eg, guideline suggestions, advisory material provisions, and responses to simple queries), tele-case management (eg, sharing information across facilities and teams), and tele-education (eg, patient self-management). However, the structure of CDSSs, such as data input, tasks, output, and intended users or decision-makers, varied. With limited studies examining varying clinical outcomes, there was inconsistent evidence of the clinical effectiveness of the interventions. Telemedicine and CDSSs have a role in supporting patients with multimorbidity. CDSSs can likely be integrated into telehealth services to improve the quality and accessibility of care. However, issues surrounding such interventions need to be further explored. These issues include expanding the spectrum of medical conditions examined; examining tasks of CDSSs, particularly for screening and diagnosis of multiple conditions; and exploring the role of the patient as the direct user of the CDSS.

PurposeThe picture archiving and communication system (PACS) is one of the most important tools used in patient care in many hospitals worldwide. It allows clinicians to remotely communicate and consult with other clinicians on patient cases and view diagnostic images from different angles, thus facilitating patient diagnosis and treatment. Several studies have been conducted in Saudi Arabia to evaluate different aspects of PACS use; however, no comprehensive study has been conducted in its Eastern Province. This study aimed to investigate clinicians’ perceptions of the advantages and disadvantages of the use of PACS in Eastern Province hospitals in Saudi Arabia and identify the factors that affect their perceptions and its use. In addition, it aimed to gather recommendations of clinicians for improving the system and its implementation.MethodsA qualitative approach with grounded theory method was employed. A sample of 18 residents, radiologists, and consultants from three Eastern Province hospitals in Saudi Arabia participated in the study. Data were collected using semi-structured interviews over a period of 7 months.ResultsThe perceived advantages of PACS included providing quality images and the ability to manipulate their resolution, whereas the perceived barriers included low-speed internet connections and technical problems. Participants recommended providing clinicians remote access to the system and implementing a mobile PACS application. The theory that emerged from the analysis revealed that demographic, system-related, and hospital-related factors affected participants’ perspectives of PACS and its use.ConclusionThe results of this study and its theoretical model can help identify areas of improvement and inform policy and strategic planning for the effective implementation of PACS in patient care in Saudi Arabia.

The American Diabetes Association (ADA) and the American Heart Association launched a multi-faceted national partnership in 2018, Know Diabetes by Heart™, to reduce cardiovascular disease (CVD) deaths, heart attacks and strokes in people living with type 2 diabetes. To support this initiative, the ADA’s Diabetes INSIDE® (DI) program formed a mid-Atlantic quality improvement (QI) collaborative of five academic medical centers and a health information exchange (HIE) with partners from primary care, endocrinology, cardiology, nephrology, nursing, pharmacy, health IT, diabetes education, population health and industry. The collaborative’s goal is to accelerate clinical adoption of recent CVD findings using population health analytics, care system redesign, shared learning, provider and patient training, education, and community resources. DI works with each partner to identify, design and monitor quantifiable QI programs to address care quality gaps. The collaborative shared their initial findings and goals at a November 2019 meeting. Three systems identified opportunities to improve low use of SGLT2 inhibitors and GLP-1 agonists in eligible patients (∼12%) or departmental prescribing gaps between primary care and cardiology vs. endocrinology (SGLT2 5% vs. 11%; GLP-1 7.5% vs. 24.6%). Other gaps included coordinating care teams and communication between inpatient and outpatient settings both within and across overlapping health systems, a gap the HIE aims to address. Other systems are implementing DM screening in cardiology clinics or training cardiologists to assume a larger role in managing patients with DM and CVD. A system with a large racial/ethnic minority population is qualitatively evaluating the social, cultural, economic, and literacy factors that facilitate or inhibit optimal care. This regional collaborative across overlapping health systems offers increased opportunity to accelerate improvement of DM and CVD care for all patients and may serve as a model for other regions. Disclosure R.E. Furman: None. F. Hill-Briggs: None. A. Iwamaye: None. C. Knight: None. T. Kouvatsos: None. Y. Lev: None. I. Lorincz: None. W.M. Marella: None. D.J. Rubin: None. M.H. Schutta: Employee; Spouse/Partner; Merck & Co., Inc. D.A. Swavely: None. L. Ward: None. E.C. Furman: None. G. Liptak: Employee; Spouse/Partner; Janssen Pharmaceuticals, Inc. Funding Know Diabetes By Heart™; Boehringer Ingelheim and Eli Lilly and Company Diabetes Alliance; Novo Nordisk; Sanofi; AstraZeneca; Bayer

BackgroundIn silico evaluation (ISE) methods create a digital twin or a computer simulation of actual care pathways, enabling a broader assessment of the potential impact of algorithm-based clinical decision support systems (CDSS) before implementation. A programmatic search of several academic research databases showed at least 886 CDSS development and evaluation studies in the past 3 decades. However, fewer than 3% applied ISE to evaluate the potential impact on broader clinical care pathways.ObjectiveThis study aims to review the scope of proposed ISE methods to evaluate CDSS, with a focus on simulation modeling approaches, care pathway parameters considered, and outcomes evaluated within the ISE methodological domain.MethodsThis review followed the established scoping review methodological guidelines. We conceptualized a tailored search framework and conducted a 2-stage screening process on studies identified through automated searches of selected databases. Relevant information on CDSS study characteristics and the application of care pathway simulation modeling in CDSS evaluation was subsequently extracted.ResultsA small subset of studies on CDSS development conducted ISE. Most ISE studies were published after 2019, reflecting a more recent increase in the application of ISE. These studies frequently emphasized patient, process, and cost-effectiveness outcomes. Notably, the evaluation of outcomes directly related to care providers’ well-being is lacking, highlighting a critical gap in current ISE applications. Among the studies included in this review, various simulation modeling paradigms were used, including dynamic simulations and state-based models. Three themes were found among the different motivations and objectives for using ISE: (1) outcome comparison, (2) outcome comparison with sensitivity analysis, and (3) simulation-based optimization of the proposed CDSS. The first two approaches considered a decoupled CDSS model training followed by simulation-based evaluation of the trained CDSS model. The third approach iteratively improved the CDSS model’s decision-support capabilities through optimization based on care pathway simulations. These approaches can be broadly categorized as decoupled (1 and 2), where CDSS models are evaluated within seperately designed clinical workflow simulations, and integrated (3), where simulation iteratively optimizes CDSS performance.ConclusionsThe growing body of algorithm-based CDSS research underscores the need for evaluation approaches that are resource-efficient and account for systems-level workflow implications. This review highlights both the gaps and the potential of ISE, particularly care pathway simulation-based approaches, as a preimplementation strategy to strengthen evidence before significant resource allocations to pilot or full-scale implementations. ISE presents as a promising intermediary evaluation approach that bridges model-level performance and clinical workflow impact, allowing more contextualized and resource-efficient assessments prior to implementation.

Theme: Data and Linkage QualityObjectives:
 
 To define health data quality from clinical, data science, and health system perspectives
 To describe some of the international best practices related to quality and how they are being applied to Canada’s administrative health data.
 To compare methods for health data quality assessment and improvement in Canada (automated logical checks, chart quality indicators, reabstraction studies, coding manager perspectives)
 To highlight how data linkage can be used to provide new insights into the quality of original data sources
 To highlight current international initiatives for improving coded data quality including results from current ICD-11 field trials
 
 Dr. Keith Denny: Director of Clinical Data Standards and Quality, Canadian Insititute for Health Information (CIHI), Adjunct Research Professor, Carleton University, Ottawa, ON. He provides leadership for CIHI’s information quality initiatives and for the development and application of clinical classifications and terminology standards.
 Maureen Kelly: Manager of Information Quality at CIHI, Ottawa, ON. She leads CIHI’s corporate quality program that is focused on enhancing the quality of CIHI’s data sources and information products and to fostering CIHI’s quality culture.
 Dr. Cathy Eastwood: Scientific Manager, Associate Director of Alberta SPOR Methods & Development Platform, Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB. She has expertise in clinical data collection, evaluation of local and systemic data quality issues, disease classification coding with ICD-10 and ICD-11.
 Dr. Hude Quan: Professor, Community Health Sciences, Cumming School of Medicine, University of Calgary, Director Alberta SPOR Methods Platform; Co-Chair of Hypertension Canada, Co-Chair of Person to Population Health Collaborative of the Libin Cardiovascular Institute in Calgary, AB. He has expertise in assessing, validating, and linking administrative data sources for conducting data science research including artificial intelligence methods for evaluating and improving data quality.
 Intended Outcomes:“What is quality health data?” The panel of experts will address this common question by discussing how to define high quality health data, and measures being taken to ensure that they are available in Canada. Optimizing the quality of clinical-administrative data, and their use-value, first requires an understanding of the processes used to create the data.
 Subsequently, we can address the limitations in data collection and use these data for diverse applications. Current advances in digital data collection are providing more solutions to improve health data quality at lower cost.
 This panel will describe a number of quality assessment and improvement initiatives aimed at ensuring that health data are fit for a range of secondary uses including data linkage. It will also discuss how the need for the linkage and integration of data sources can influence the views of the data source’s fitness for use.
 CIHI content will include:
 
 Methods for optimizing the value of clinical-administrative data
 CIHI Information Quality Framework
 Reabstraction studies (e.g. physician documentation/coders’ experiences)
 Linkage analytics for data quality
 
 University of Calgary content will include:
 
 Defining/measuring health data quality
 Automated methods for quality assessment and improvement
 ICD-11 features and coding practices
 Electronic health record initiatives

Big data refers to the data sets that have become so large and/or complex that traditional data technology is inadequate to process them effectively. Big data has large volume; changes quickly; has great variety; and has a good deal of uncertainty in its veracity. During the digitization of healthcare, hospitals become important sources of big data. Large volume of data is created from medical records, medical images, and distance medicine. For healthcare industry, big data analytics are evolving beyond traditional business intelligence and clinical decision support systems. It utilizes the vast amount of data gathered from healthcare networks for evidence-based medicine, propels precision medicine, health management, disease prevention, even the establishment and application of biobanks. This paper summarizes the current composition, characteristics, and trend of big data. It introduces the framework, tools, and methods for big data analytics. Key words: Big data analytics; Big data management; Precision medicine; Disease prevention

Introduction: The Coronavirus Disease-2019 (COVID-19) pandemic has caused global disruption, putting health experts and healthcare systems at risk. However, the ultimate goal of medical systems to offer precise medical care in a holistic manner for the benefit of patients cannot be compromised. With the massive advancements in digital technology, it is now possible for healthcare systems and medical practitioners to handle and utilise enormous amounts of patient data to provide appropriate medical assistance with minimal error. Integrated with Electronic Health Records (EHR), Clinical Decision Support Systems (CDSS) are digital programs that analyse patient data and assist medical professionals in making decisions and recommendations, thereby enhancing patient care. CDSS have played a critical role in maximising care during the pandemic by helping clinicians offer evidence-based medical care using patient data, paving the way for a more accurate and personalised healthcare delivery. However, their extended usability to manage post-COVID-19 conditions remains unexplored. Aim: This scoping review seeks to outline the use of these CDSS in the management of COVID-19 and their potential usability in risk assessment, severity prediction, and treatment goals for patients experiencing long COVID-19 symptoms. Materials and Methods: A thorough literature search was conducted on Google Scholar and PubMed using key search terms to identify relevant articles that support the objective of this study. A total of 3,010 records were available, of which 13 articles were chosen for inclusion in this review after extensive screening in accordance with the eligibility standards set up. The supporting data were meticulously extracted and charted to provide a clear outline of the utility of CDSS. This scoping review also features a conceptual framework for the extended usability of CDSS in long COVID-19 management. Results: After the methodological search and selection process, data from 13 articles were analysed. Most of the included studies were conducted in the United States. The majority of the CDSS were designed to assess the severity of COVID-19. These CDSS predominantly analysed blood investigations, COVID-19 symptoms, and radiological findings of patients to make appropriate clinical decisions for managing the disease. There was a lack of scientific literature supporting the use of CDSS in long COVID-19 management. Conclusion: With healthcare systems dealing with massive amounts of patient data, especially during the pandemic and postpandemic crisis, appropriate methods to manage and handle this information are critical to delivering patient-centered medical care. CDSS have been widely utilised in this regard to enhance the health outcomes of patients by guiding health professionals to make the right treatment choices in the most evidence-based manner using patients’ health data. Thus, given the future of healthcare systems with Artificial Intelligence (AI), a greater emphasis on expanding the usability of these CDSS beyond the scope of COVID-19 is essential.

Clinical decision support (CDS) has the potential to improve clinical decision-making consistent with evidence-based care. CDS can be designed to save health care providers time and help them provide safe and personalized analgesic prescribing. The aim of this report is to describe the development of a CDS system designed to provide dentists with personalized pain management recommendations to reduce opioid prescribing following extractions. The use of CDS is also examined. This study was conducted in HealthPartners, which uses an electronic health record (EHR) system that integrates both medical and dental information upon which the CDS application was developed based on SMART (Substitutable Medical Applications and Reusable Technologies) on FHIR (Fast Healthcare Interoperability Resources). The various tools used to bring relevant medical conditions, medications, patient history, and other relevant data into the CDS interface are described. The CDS application runs a drug interaction algorithm developed by our organization and provides patient-specific recommendations. The CDS included access to the state Prescription Monitoring Program database. The pain management CDS was implemented as part of a study examining opioid prescribing among patients undergoing dental extraction procedures from February 17, 2020, to May 14, 2021. Provider-level use of CDS at extraction encounters ranged from 0% to 87.4% with 12.1% of providers opening the CDS for no encounters, 39.4% opening the CDS for 1%-20% of encounters, 36.4% opening it for 21%-50% of encounters, and 12.1% opening it for 51%-87% of encounters. The pain management CDS is an EHR-embedded, provider-facing tool to help dentists make personalized pain management recommendations following dental extractions. The SMART on FHIR-based pain management CDS adapted well to the point-of-care dental setting and led to the design of a scalable CDS tool that is EHR vendor agnostic. ClinicalTrials.gov NCT03584789; https://clinicaltrials.gov/study/NCT03584789.

Introduction In 2023, our center implemented a novel clinical decision support system (CDSS) for burn resuscitation. Initial education was provided to staff and performance was monitored in real-time. It was found that burn nursing staff (RN) were struggling with the components of CDSS documentation despite ongoing education. Recognizing that RNs lacked understanding, confidence and comfort with the system, the team revamped the CDSS education. A quality improvement (QI) project was initiated using a multi-pronged educational approach. The purpose of this study was to evaluate the efficacy of the project in improving nurse-reported levels of comfort and confidence. Methods The Burn Educator and Burn Nurse Specialist worked to expand the existing education mediums which included nursing huddles and instructional handouts. Complementary resources were developed, namely classroom training, an enhanced step-by-step guide, and initiation checklist. Learners were given pre- and post-education surveys to ascertain comfort and confidence in CDSS initiation, ongoing management, troubleshooting, location of resources and serving as a resource for colleagues. A post-education test was administered with a minimum score of 100% required to pass. Results A total of 33 RNs completed the training. The greatest improvement in comfort and confidence was seen in the management of resuscitation in the operating room (pre 30.3%, post 78.8%), followed by initiation and troubleshooting (pre 48.5%, post 93.9%), supporting colleagues with CDSS use (pre 48.5%, post 72.7%), and ability locating resources (pre 69.7%, post 97%). Ultimately, all RNs passed the post-test with the required 100% score. Conclusions The most notable improvements were seen in comfort initiating CDSS-guided fluid resuscitation and continuing resuscitation off-unit. These measures of amplified comfort and confidence, combined with increased levels of understanding ultimately impact nursing competency. All burn ICU and ED RNs are required to complete and pass the education prior to caring for a patient during fluid resuscitation in our center. The education is required annually and routine monitoring of performance is incorporated into our formal QI program. Applicability of Research to Practice This project highlights the importance of expanding education to mediate fluctuating levels of RN comfort, confidence and understanding. Funding for the Study N/A

Chapter One - Overview of effective pharmacogenomic clinical decision support