Electronic medication reconciliation and medication errors.

OBJECTIVE: To measure the impact of electronic medication reconciliation implementation on reports of admission medication reconciliation errors (MREs). DESIGN: Quality improvement project with time-series design. SETTING: A large, urban, tertiary care children's hospital. PARTICIPANTS: All admitted patients from 2011 and 2012. INTERVENTIONS: Implementation of an electronic medication reconciliation tool for hospital admissions and regular compliance reporting to inpatient units. The tool encourages active reconciliation by displaying the pre-admission medication list and admission medication orders side-by-side. MAIN OUTCOME MEASURE: Rate of non-intercepted admission MREs identified via a voluntary reporting system. RESULTS: During the study period, there were 33 070 hospital admissions. The pre-admission medication list was consistently recorded electronically throughout the study period. In the post-intervention period, the use of the electronic medication reconciliation tool increased to 84%. Reports identified 146 admission MREs during the study period, including 95 non-intercepted errors. Pre- to post-intervention, the rate of non-intercepted errors decreased by 53% (P = 0.02). Reported errors were categorized as intercepted potential adverse drug events (ADEs) (35%), non-intercepted potential ADEs (42%), minor ADEs (22%) or moderate ADEs (1%). There were no reported MREs that resulted in major or catastrophic ADEs. CONCLUSIONS: We successfully implemented an electronic process for admission medication reconciliation, which was associated with a reduction in reports of non-intercepted admission MREs.

Job search strategies and early careers of clinical informatics fellowship alumni (2016-2022).

OBJECTIVE: To report on clinical informatics (CI) fellows' job search and early careers. MATERIALS AND METHODS: In the summer of 2022, we performed a voluntary and anonymous survey of 242 known clinical informatics fellowship alumni from 2016 to 2022. The survey included questions about their initial job search process; first job, salary, and informatics time after training; and early career progression over the first 1-6 years after fellowship. RESULTS: Nearly half (101, 41.7%) responded to the survey. Median informatics time was 50%; most were compensated similar/better than a purely clinical position. Most reported CI fellowship significantly impacted their career, were satisfied with their first and current job after training, and provided advice for current fellows and CI education leaders. Graduates in 2022 had a median job search of 5 months, beginning 3-15 months before graduation; most had a position created for them. Nearly all graduates from 2016-2021 (61, 93.8%) had at least one change in roles/benefits since finishing training, with a trend for increased informatics time and salary. DISCUSSION: There was a wide variety of roles, salary, and funding sources for CI positions. This highlights some of the unique challenges CI fellows face and the importance of networking. These results will help CI education leaders, fellows, alumni, and prospective fellowship applicants. CONCLUSION: Graduates felt that CI fellowship had a significant impact on their career, were pleased with their first jobs and early career trajectory. Continued follow-up of the experience of new graduates and alumni is needed to assess emerging patterns over time.

An Algorithm to Assess Guideline Concordance of Antibiotic Choice in Community-Acquired Pneumonia.

BACKGROUND AND OBJECTIVE: This study aimed to develop and evaluate an algorithm to reduce the chart review burden of improvement efforts by automatically labeling antibiotic selection as either guideline-concordant or -discordant based on electronic health record data for patients with community-acquired pneumonia (CAP). METHODS: We developed a 3-part algorithm using structured and unstructured data to assess adherence to an institutional CAP clinical practice guideline. The algorithm was applied to retrospective data for patients seen with CAP from 2017 to 2019 at a tertiary children's hospital. Performance metrics included positive predictive value (precision), sensitivity (recall), and F1 score (harmonized mean), with macro-weighted averages. Two physician reviewers independently assigned "actual" labels based on manual chart review. RESULTS: Of 1345 patients with CAP, 893 were included in the training cohort and 452 in the validation cohort. Overall, the model correctly labeled 435 of 452 (96%) patients. Of the 286 patients who met guideline inclusion criteria, 193 (68%) were labeled as having received guideline-concordant antibiotics, 48 (17%) were labeled as likely in a scenario in which deviation from the clinical practice guideline was appropriate, and 45 (16%) were given the final label of "possibly discordant, needs review." The sensitivity was 0.96, the positive predictive value was 0.97, and the F1 was 0.96. CONCLUSIONS: An automated algorithm that uses structured and unstructured electronic health record data can accurately assess the guideline concordance of antibiotic selection for CAP. This tool has the potential to improve the efficiency of improvement efforts by reducing the manual chart review needed for quality measurement.

Performance of ChatGPT and Google Translate for Pediatric Discharge Instruction Translation.

BACKGROUND AND OBJECTIVES: Patients who speak languages other than English face barriers to equitable healthcare delivery. Machine translation systems, including emerging large language models, have the potential to expand access to translation services, but their merits and limitations in clinical practice remain poorly defined. We aimed to assess the performance of Google Translate and ChatGPT for multilingual translation of pediatric discharge instructions. METHODS: Twenty standardized discharge instructions for pediatric conditions were translated into Spanish, Brazilian Portuguese, and Haitian Creole by professional translation services, Google Translate and ChatGPT-4.0, and evaluated for adequacy (preserved information), fluency (grammatical correctness), meaning (preserved connotation), and severity (clinical harm), along with assessment of overall preference. Domain-level ratings and preferred translation source were summarized with descriptive statistics and compared with professional translations. RESULTS: Google Translate and ChatGPT demonstrated similar domain-level ratings to professional translations for Spanish and Portuguese. For Haitian Creole, compared with both Google Translate and ChatGPT, professional translations demonstrated significantly greater adequacy, fluency meaning, and severity scores. ChatGPT (33.3%, P < .001) and Google Translate (23.3%, P = .024) contained more potentially clinically significant errors (severity score ≤3) for Haitian Creole than professional translations (8.3%). Professional Haitian Creole (48.3%) and Portuguese (43.3%), but not Spanish (15%), translations were most frequently preferred among translation sources. CONCLUSIONS: Machine translation platforms have comparable performance to professional translations for Spanish and Portuguese but shortcomings in quality, accuracy, and preference persist for Haitian Creole. Diverse multilingual training data are needed, along with regulations ensuring safe and equitable applications of machine translation in clinical practice.

ChatGPT Use Among Pediatric Health Care Providers: Cross-Sectional Survey Study.

BACKGROUND: The public launch of OpenAI's ChatGPT platform generated immediate interest in the use of large language models (LLMs). Health care institutions are now grappling with establishing policies and guidelines for the use of these technologies, yet little is known about how health care providers view LLMs in medical settings. Moreover, there are no studies assessing how pediatric providers are adopting these readily accessible tools. OBJECTIVE: The aim of this study was to determine how pediatric providers are currently using LLMs in their work as well as their interest in using a Health Insurance Portability and Accountability Act (HIPAA)-compliant version of ChatGPT in the future. METHODS: A survey instrument consisting of structured and unstructured questions was iteratively developed by a team of informaticians from various pediatric specialties. The survey was sent via Research Electronic Data Capture (REDCap) to all Boston Children's Hospital pediatric providers. Participation was voluntary and uncompensated, and all survey responses were anonymous. RESULTS: Surveys were completed by 390 pediatric providers. Approximately 50% (197/390) of respondents had used an LLM; of these, almost 75% (142/197) were already using an LLM for nonclinical work and 27% (52/195) for clinical work. Providers detailed the various ways they are currently using an LLM in their clinical and nonclinical work. Only 29% (n=105) of 362 respondents indicated that ChatGPT should be used for patient care in its present state; however, 73.8% (273/368) reported they would use a HIPAA-compliant version of ChatGPT if one were available. Providers' proposed future uses of LLMs in health care are described. CONCLUSIONS: Despite significant concerns and barriers to LLM use in health care, pediatric providers are already using LLMs at work. This study will give policy makers needed information about how providers are using LLMs clinically.

Toward a responsible future: recommendations for AI-enabled clinical decision support.

BACKGROUND: Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging. OBJECTIVES: This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients. MATERIALS AND METHODS: In May 2023, the Division of Clinical Informatics at Beth Israel Deaconess Medical Center and the American Medical Informatics Association co-sponsored a working group on AI in healthcare. In August 2023, there were 4 webinars on AI topics and a 2-day workshop in September 2023 for consensus-building. The event included over 200 industry stakeholders, including clinicians, software developers, academics, ethicists, attorneys, government policy experts, scientists, and patients. The goal was to identify challenges associated with the trusted use of AI-enabled CDS in medical practice. Key issues were identified, and solutions were proposed through qualitative analysis and a 4-month iterative consensus process. RESULTS: Our work culminated in several key recommendations: (1) building safe and trustworthy systems; (2) developing validation, verification, and certification processes for AI-CDS systems; (3) providing a means of safety monitoring and reporting at the national level; and (4) ensuring that appropriate documentation and end-user training are provided. DISCUSSION: AI-enabled Clinical Decision Support (AI-CDS) systems promise to revolutionize healthcare decision-making, necessitating a comprehensive framework for their development, implementation, and regulation that emphasizes trustworthiness, transparency, and safety. This framework encompasses various aspects including model training, explainability, validation, certification, monitoring, and continuous evaluation, while also addressing challenges such as data privacy, fairness, and the need for regulatory oversight to ensure responsible integration of AI into clinical workflow. CONCLUSIONS: Achieving responsible AI-CDS systems requires a collective effort from many healthcare stakeholders. This involves implementing robust safety, monitoring, and transparency measures while fostering innovation. Future steps include testing and piloting proposed trust mechanisms, such as safety reporting protocols, and establishing best practice guidelines.

Structure and Funding of Clinical Informatics Fellowships: A National Survey of Program Directors.

BACKGROUND: In 2011, the American Board of Medical Specialties established clinical informatics (CI) as a subspecialty in medicine, jointly administered by the American Board of Pathology and the American Board of Preventive Medicine. Subsequently, many institutions created CI fellowship training programs to meet the growing need for informaticists. Although many programs share similar features, there is considerable variation in program funding and administrative structures. OBJECTIVES: The aim of our study was to characterize CI fellowship program features, including governance structures, funding sources, and expenses. METHODS: We created a cross-sectional online REDCap survey with 44 items requesting information on program administration, fellows, administrative support, funding sources, and expenses. We surveyed program directors of programs accredited by the Accreditation Council for Graduate Medical Education between 2014 and 2021. RESULTS: We invited 54 program directors, of which 41 (76%) completed the survey. The average administrative support received was $27,732/year. Most programs (85.4%) were accredited to have two or more fellows per year. Programs were administratively housed under six departments: Internal Medicine (17; 41.5%), Pediatrics (7; 17.1%), Pathology (6; 14.6%), Family Medicine (6; 14.6%), Emergency Medicine (4; 9.8%), and Anesthesiology (1; 2.4%). Funding sources for CI fellowship program directors included: hospital or health systems (28.3%), clinical departments (28.3%), graduate medical education office (13.2%), biomedical informatics department (9.4%), hospital information technology (9.4%), research and grants (7.5%), and other sources (3.8%) that included philanthropy and external entities. CONCLUSION: CI fellowships have been established in leading academic and community health care systems across the country. Due to their unique training requirements, these programs require significant resources for education, administration, and recruitment. There continues to be considerable heterogeneity in funding models between programs. Our survey findings reinforce the need for reformed federal funding models for informatics practice and training.

A Year of Inpatient Telehealth: Lessons From the COVID-19 Pandemic.

BACKGROUND: Relaxation of telehealth regulation enforcement during the coronavirus disease 2019 pandemic opened the door to massive expansion. Here we describe inpatient telehealth usage across a pediatric academic hospital during the first year of the pandemic. METHODS: We created hospital bed-specific inpatient telehealth accounts and monitored their use over a 1 year period using data from our video conferencing vendor. We matched data with our enterprise data warehouse based on session date and time to identify patients who participated in telehealth. We performed secondary analysis of all video conferences to identify additional multidisciplinary team and family meetings that did not leverage the bed-specific telehealth accounts. RESULTS: We hosted 6931 inpatient telehealth sessions associated with 1648 unique patients. Hospitalized patients participating in telehealth sessions were older and had markedly longer length of stay compared with those who did not use telehealth (median age 12 vs 8 years, P < .001; median length of stay 9.03 vs 2.03 days, P < .001). There were 2006 charges for telehealth sessions, half of which were from psychiatry providers. Secondary analysis revealed an additional 1132 sessions used for interdisciplinary team or family meetings. CONCLUSIONS: Clinicians used inpatient telehealth to support care of hospitalized pediatric patients during the coronavirus disease pandemic, particularly for mental health care and family meetings. These findings suggest ongoing opportunities for inpatient telehealth systems beyond the pandemic.

Creation and Evaluation of a Clinical Informatics Match: Initial Findings.

BACKGROUND: Clinical Informatics (CI) fellowship programs utilize the Electronic Residency Application Service (ERAS) to gather applications but until recently used an American Medical Informatics Association (AMIA) member-developed, simultaneous offer-acceptance process to match fellowship applicants to programs. In 2021, program directors collaborated with the AMIA to develop a new match to improve the process. OBJECTIVE: Describe the results of the first 2 years of the match and address opportunities for improvement. METHODS: We obtained applicant data for fellowship applicants in 2021 and 2022 from the ERAS and match data for the same years from the AMIA. We analyzed our data using descriptive statistics. RESULTS: There were 159 unique applicants over the 2-year period. Applicants submitted 2,178 applications with a median of 10 per applicant (interquartile range [IQR] 3-20). One hundred and four applicants (65.4%) participated in the match and ranked a median of seven programs (2-12). Forty-two programs in 2021 and 47 programs in 2022 offered a combined total 153 positions in the match. Participating programs ranked a median of eight applicants per year (IQR 5-11). Of participating applicants, 95 (91.3%) successfully matched and of those 66 (69.5%) received their top choice. Thirty-two programs (76.2%) matched at least one candidate in 2021 and 33 programs (70.2%) matched at least one candidate in 2022. In both years, 24 programs filled all available slots (57.1% in 2021 and 51.1% in 2022). CONCLUSION: Applicants were extremely successful in the new match, which successfully addressed most of the challenges of the simultaneous offer-acceptance process identified by program directors. However, applicant attrition resulted in a quarter of programs going unmatched. Although many programs still filled slots outside the match, fellowship slots may remain unfilled while the CI practice pathway remains open.

A Model for Work Intensity in a Pediatric Training Program.

Background: The Accreditation Council for Graduate Medical Education (ACGME) requires residency programs to monitor scheduling, work intensity, and work compression. Objective: We aimed to create a model for assessing intern work intensity by examining patient and clinical factors in our electronic health systems using multiple linear regression. Methods: We identified measurable factors that may contribute to resident work intensity within our electronic health systems. In the spring of 2021, we surveyed interns on pediatric hospital medicine rotations each weekday over 5 blocks to rank their daily work intensity on a scale from -100 (bored) to +100 (exasperated). We queried our electronic systems to identify patient care activities completed by study participants on days they were surveyed. We used multiple linear regression to identify factors that correlate with subjective scores of work intensity. Results: Nineteen unique interns provided 102 survey responses (28.3% response rate) during the study period. The mean work intensity score was 9.82 (SD=44.27). We identified 19 candidate variables for the regression model. The most significantly associated variables from our univariate regression model were text messages (ß=0.432, P<.0009, R2=0.105), orders entered (ß=0.207, P<.0002, R2=0.128), and consults ordered (ß=0.268, P=.022, R2=0.053). Stepwise regression produced a reduced model (R2=0.247) including text messages (ß=0.379, P=.002), patient transfers (ß=-1.405, P=.15), orders entered (ß=0.186, P<.001), and national patients (ß=-0.873, P=.035). Conclusions: Our study demonstrates that data extracted from electronic systems can be used to estimate resident work intensity.

Inpatient Telehealth Experience of Patients With Limited English Proficiency: Cross-sectional Survey and Semistructured Interview Study.

BACKGROUND: Patients with limited English proficiency (LEP) are at a higher risk of poor health outcomes and are less likely to use telehealth than English-speaking patients. To date, there is no formal evaluation of inpatient (IP) telehealth user experience of patients and their families by language preference during visits with their clinicians. OBJECTIVE: This study aims to compare the experiences of English- and Spanish-speaking patients and their families using IP telehealth, as well as to evaluate the experience of Spanish interpreters providing services through IP telehealth. METHODS: We prospectively administered a survey to English- and Spanish-speaking patients and their families who used IP telehealth from October 1, 2020, to March 31, 2021. We performed semistructured phone interviews of hospital-based Spanish interpreters who provided services through IP telehealth. RESULTS: A total of 661 surveys were administered, with completion rates of 18% (112/621) in English and 62% (25/40) in Spanish. On a 10-point scale, the overall satisfaction of Spanish speakers (median 10, IQR 10-10) was higher than that of English speakers (median 9, IQR 8-10; P=.001). Both English- and Spanish-speaking patients used IP telehealth for visits with their primary IP care team, subspecialty consultants, and other clinicians. Hospital tablets were used more often than personal devices, and only English-speaking patients used personal laptops. Patients and their families encountered challenges with log-in, team coordination with multiple users, and equipment availability. Interpreters encountered challenges with audio and video quality, communication, safety, and Wi-Fi access. CONCLUSIONS: Both English- and Spanish-speaking patients reported high satisfaction using IP telehealth across multiple disciplines despite the workflow challenges identified by interpreters. Significant investment is needed to provide robust infrastructure to support use by all patients, especially the integration of multiple users to provide interpreter services for patients with LEP.

Pediatric Intern Clinical Exposure During the COVID-19 Pandemic.

BACKGROUND AND OBJECTIVES: Pediatric health care encounters declined during the coronavirus disease 2019 (COVID-19) pandemic, and pediatric residency programs have adapted trainee schedules to meet the needs of this changing clinical environment. We sought to evaluate the impact of the pandemic on pediatric interns' clinical exposure. METHODS: In this retrospective cohort study, we quantified patient exposure among pediatric interns from a single large pediatric residency program at a freestanding children's hospital. Patient encounters and shifts per pediatric intern in the inpatient and emergency department settings were evaluated during the COVID-19 pandemic, from March to June 2020, as compared with these 3 months in 2019. Patient encounters by diagnosis were also evaluated. RESULTS: The median number of patient encounters per intern per 2-week block declined on the pediatric hospital medicine service (37.5 vs 27.0; P < .001) and intensive care step-down unit (29.0 vs 18.8; P = .004) during the pandemic. No significant difference in emergency department encounters was observed (63.0 vs 40.5; P = .06). The median number of shifts worked per intern per 2-week block also decreased on the pediatric hospital medicine service (10.5 vs 9.5, P < .001). Across all settings, there were more encounters for screening for infectious disease and fewer encounters for respiratory illnesses. CONCLUSIONS: Pediatric interns at the onset of the COVID-19 pandemic were exposed to fewer patients and had reduced clinical schedules. Careful consideration is needed to track and supplement missed clinical experiences during the pandemic.

Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic.

OBJECTIVE: This work examined the secondary use of clinical data from the electronic health record (EHR) for screening our healthcare worker (HCW) population for potential exposures to patients with coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: We conducted a cross-sectional study at a free-standing, quaternary care pediatric hospital comparing first-degree, patient-HCW pairs identified by the hospital's COVID-19 contact tracing team (CTT) to those identified using EHR clinical event data (EHR Report). The primary outcome was the number of patient-HCW pairs detected by each process. RESULTS: Among 233 patients with COVID-19, our EHR Report identified 4116 patient-HCW pairs, including 2365 (30.0%) of the 7890 pairs detected by the CTT. The EHR Report also revealed 1751 pairs not identified by the CTT. The highest number of patient-HCW pairs per patient was detected in the inpatient care venue. Nurses comprised the most frequently identified HCW role overall. CONCLUSIONS: Automated methods to screen HCWs for potential exposures to patients with COVID-19 using clinical event data from the EHR (1) are likely to improve epidemiological surveillance by contact tracing programs and (2) represent a viable and readily available strategy that should be considered by other institutions.

SH2D1A regulates T-dependent humoral autoimmunity.

The signaling lymphocytic activation molecule (SLAM)/CD150 family includes a family of chromosome 1-encoded cell surface molecules with costimulatory functions mediated in part by the adaptor protein SH2D1A (SLAM-associated protein, SAP). Deficiency in SH2D1A protects mice from an experimental model of lupus, including the development of hypergammaglobulinemia, autoantibodies including anti-double stranded DNA, and renal disease. This protection did not reflect grossly defective T or B cell function per se because SH2D1A-deficient mice were susceptible to experimental autoimmune encephalomyelitis, a T cell-dependent disease, and they were capable of mounting normal T-independent antigen-specific immunoglobulin responses. Instead, T-dependent antibody responses were impaired in SH2D1A-deficient mice, reflecting defective germinal center formation. These findings demonstrate a specific role for the SLAM-SH2D1A system in the regulation of T-dependent humoral immune responses, implicating members of the CD150-SH2D1A family as targets in the pathogenesis and therapy of antibody-mediated autoimmune and allergic diseases.

Have you got the time? Challenges using vendor electronic health record metrics of provider efficiency.

The rise of clinician burnout has been correlated with the increased adoption of electronic health records (EHRs). Some vendors have used data entry logs to measure the amount of time spent using the EHR and have developed metrics of provider efficiency. Initial attempts to utilize these data have proven difficult as it is not always apparent whether variations reflect provider behavior or simply the metric definitions. Metric definitions are also updated intermittently without warning, making longitudinal assessment problematic. Because the metrics are based on proprietary algorithms, they are impossible to validate without costly time-motion studies and are also difficult to compare across institutions and vendors. Clinical informaticians must partner with vendors in order to develop industry standards of EHR use, which could then be used to examine the impact of EHRs on clinician burnout.

Preserving privacy for pediatric patients and families: use of confidential note types in pediatric ambulatory care.

OBJECTIVE: This study evaluates and characterizes the use of a confidential clinic note type as part of the implementation of open notes at a free-standing children's hospital. We describe how this electronic health record feature which disables patient and family access to selected notes in the patient portal is used across our institution, which clinicians are using this feature, and the type of data our clinicians consider confidential. MATERIALS AND METHODS: Through retrospective chart review, we have evaluated the use of a confidential note type over a 1-year period. RESULTS: We identified 402 964 clinic notes created during a 1-year period, of which 9346 (2.3%) were flagged as confidential. Use of this confidential note type was associated with female patient sex and increase in patient age. It was used most frequently by a small subset of providers. 922 (83.8%) of 1100 notes manually reviewed contained sensitive information. Reasons for confidential notes varied, but patient's mental health was most commonly identified. DISCUSSION: Our data demonstrate variability in the use of a confidential note type across specialties, patient ages, and types of confidential information. This note type is frequently utilized by a subset of providers who often manage sensitive patient and parent information. As vendors and institutions enable open notes, thoughtful implementation and provider education surrounding the use of this confidential feature is needed. CONCLUSION: A confidential clinic note feature is an integral aspect of pediatric open notes implementation. This feature supports protection of confidential information pertaining to our patients and their caregivers.

Early experiences with combined fellowship training in clinical informatics.

Given the ubiquitous nature of information systems in modern health care, interest in the pursuit of formal training in clinical informatics is increasing. This interest is not restricted to generalists-informatics training is increasingly being sought by future subspecialists. The traditional structure of Accreditation Council on Graduate Medical Education subspecialty training requires completion of both clinical and clinical informatics fellowship programs, and understandably lacks appeal due to the time commitment required. One approach to encourage clinical informatics training is to integrate it with clinical fellowships in order to confer dual-board eligibility. In this perspective, we describe 3 successful petitions for combined training in clinical informatics in order to support other programs and the American Board of Preventive Medicine in establishing pathways for training subspecialists in clinical informatics.

Digitizing U.S. Air Force Medical Standards for the Creation and Validation of a Readiness Decision Support System.

INTRODUCTION: Deployment-limiting medical conditions are the primary reason why service members are not medically ready. Service-specific standards guide clinicians in what conditions are restrictive for duty, fitness, and/or deployment requirements. The Air Force (AF) codifies most standards in the Medical Standards Directory (MSD). Providers manually search this document, among others, to determine if any standards are violated, a tedious and error-prone process. Digitized, standards-based decision-support tools for providers would ease this workflow. This study digitized and mapped all AF occupations to MSD occupational classes and all MSD standards to diagnosis codes and created and validated a readiness decision support system (RDSS) around this mapping. MATERIALS AND METHODS: A medical coder mapped all standards within the May 2018 v2 MSD to 2018 International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes. For the publication of new MSDs, we devised an automated update process using Amazon Web Service's Comprehend Medical and the Unified Medical Language System's Metathesaurus. We mapped Air Force Specialty Codes to occupational classes using the MSD and AF classification directories. We uploaded this mapping to a cloud-based MySQL (v5.7.23) database and built a web application to interface with it using R (v3.5+). For validation, we compared the RDSS to the record review of two subject-matter experts (SMEs) for 200 outpatient encounters in calendar year 2018. We performed four separate analyses: (1) SME vs. RDSS for any restriction; (2) SME interrater reliability for any restriction; (3) SME vs. RDSS for specific restriction(s); and (4) SME interrater reliability for categorical restriction(s). This study was approved as "Not Human Subjects Research" by the Air Force Research Laboratory (FWR20190100N) and Boston Children's Hospital (IRB-P00031397) review boards. RESULTS: Of the 709 current medical standards in the September 2019 MSD, 631 (89.0%) were mapped to ICD-10-CM codes. These 631 standards mapped to 42,810 unique ICD codes (59.5% of all active 2019 codes) and covered 72.3% (7,823/10,821) of the diagnoses listed on AF profiles and 92.8% of profile days (90.7/97.8 million) between February 1, 2007 and January 31, 2017. The RDSS identified diagnoses warranting any restrictions with 90.8% and 90.0% sensitivity compared to SME A and B. For specific restrictions, the sensitivity was 85.0% and 44.8%. The specificity was poor for any restrictions (20.5%-43.4%) and near perfect for specific restrictions (99.5+%). The interrater reliability between SMEs for all comparisons ranged from minimal to moderate (κ = 0.33-0.61). CONCLUSION: This study demonstrated key pilot steps to digitizing and mapping AF readiness standards to existing terminologies. The RDSS showed one potential application. The sensitivity between the SMEs and RDSS demonstrated its viability as a screening tool with further refinement and study. However, its performance was not evenly distributed by special duty status or for the indication of specific restrictions. With machine consumable medical standards integrated within existing digital infrastructure and clinical workflows, RDSSs would remove a significant administrative burden from providers and likely improve the accuracy of readiness metrics.

Rapid Implementation of an Inpatient Telehealth Program during the COVID-19 Pandemic.

BACKGROUND: Relaxation of laws and regulations around privacy and billing during the COVID-19 pandemic provide expanded opportunities to use telehealth to provide patient care at a distance. Many health systems have transitioned to providing outpatient care via telehealth; however, there is an opportunity to utilize telehealth for inpatients to promote physical distancing. OBJECTIVE: This article evaluates the use of a rapidly implemented, secure inpatient telehealth program. METHODS: We assembled a multidisciplinary team to rapidly design, implement, and iteratively improve an inpatient telehealth quality improvement initiative using an existing videoconferencing system at our academic medical center. We assigned each hospital bed space a unique meeting link and updated the meeting password for each new patient. Patients and families were encouraged to use their own mobile devices to join meetings when possible. RESULTS: Within 7 weeks of go-live, we hosted 1,820 inpatient telehealth sessions (13.3 sessions per 100 bedded days). We logged 104,647 minutes of inpatient telehealth time with a median session duration of 22 minutes (range 1-1,961). There were 5,288 participant devices used with a mean of 3 devices per telehealth session (range 2-22). Clinicians found they were able to build rapport and perform a reasonable physical exam. CONCLUSION: We successfully implemented and scaled a secure inpatient telehealth program using an existing videoconferencing system in less than 1 week. Our implementation provided an intuitive naming convention for providers and capitalized on the broad availability of smartphones and tablets. Initial comments from clinicians suggest the system was useful; however, further work is needed to streamline initial setup for patients and families as well as care coordination to support clinician communication and workflows. Numerous use cases identified suggest a role for inpatient telehealth will remain after the COVID-19 crisis underscoring the importance of lasting regulatory reform.

A Quality Improvement Initiative to Decrease Platelet Ordering Errors and a Proposed Model for Evaluating Clinical Decision Support Effectiveness.

BACKGROUND AND OBJECTIVES: Clinical decision support (CDS) and computerized provider order entry have been shown to improve health care quality and safety, but may also generate previously unanticipated errors. We identified multiple CDS tools for platelet transfusion orders. In this study, we sought to evaluate and improve the effectiveness of those CDS tools while creating and testing a framework for future evaluation of other CDS tools. METHODS: Using a query of an enterprise data warehouse at a tertiary care pediatric hospital, we conducted a retrospective analysis to assess baseline use and performance of existing CDS for platelet transfusion orders. Our outcome measure was the percentage of platelet undertransfusion ordering errors. Errors were defined as platelet transfusion volumes ordered which were less than the amount recommended by the order set used. We then redesigned our CDS and measured the impact of our intervention prospectively using statistical process control methodology. RESULTS: We identified that 62% of all platelet transfusion orders were placed with one of two order sets (Inpatient Service 1 and Inpatient Service 2). The Inpatient Service 1 order set had a significantly higher occurrence of ordering errors (3.10% compared with 1.20%). After our interventions, platelet transfusion order error occurrence on Inpatient Service 1 decreased from 3.10 to 0.33%. CONCLUSION: We successfully reduced platelet transfusion ordering errors by redesigning our CDS tools. We suggest that the use of collections of clinical data may help identify patterns in erroneous ordering, which could otherwise go undetected. We have created a framework which can be used to evaluate the effectiveness of other similar CDS tools.