The Safety of Outpatient Health Care : Review of Electronic Health Records.

BACKGROUND: Despite considerable emphasis on delivering safe care, substantial patient harm occurs. Although most care occurs in the outpatient setting, knowledge of outpatient adverse events (AEs) remains limited. OBJECTIVE: To measure AEs in the outpatient setting. DESIGN: Retrospective review of the electronic health record (EHR). SETTING: 11 outpatient sites in Massachusetts in 2018. PATIENTS: 3103 patients who received outpatient care. MEASUREMENTS: Using a trigger method, nurse reviewers identified possible AEs and physicians adjudicated them, ranked severity, and assessed preventability. Generalized estimating equations were used to assess the association of having at least 1 AE with age, sex, race, and primary insurance. Variation in AE rates was analyzed across sites. RESULTS: The 3103 patients (mean age, 52 years) were more often female (59.8%), White (75.1%), English speakers (90.8%), and privately insured (70.4%) and had a mean of 4 outpatient encounters in 2018. Overall, 7.0% (95% CI, 4.6% to 9.3%) of patients had at least 1 AE (8.6 events per 100 patients annually). Adverse drug events were the most common AE (63.8%), followed by health care-associated infections (14.8%) and surgical or procedural events (14.2%). Severity was serious in 17.4% of AEs, life-threatening in 2.1%, and never fatal. Overall, 23.2% of AEs were preventable. Having at least 1 AE was less often associated with ages 18 to 44 years than with ages 65 to 84 years (standardized risk difference, -0.05 [CI, -0.09 to -0.02]) and more often associated with Black race than with Asian race (standardized risk difference, 0.09 [CI, 0.01 to 0.17]). Across study sites, 1.8% to 23.6% of patients had at least 1 AE and clinical category of AEs varied substantially. LIMITATION: Retrospective EHR review may miss AEs. CONCLUSION: Outpatient harm was relatively common and often serious. Adverse drug events were most frequent. Rates were higher among older adults. Interventions to curtail outpatient harm are urgently needed. PRIMARY FUNDING SOURCE: Controlled Risk Insurance Company and the Risk Management Foundation of the Harvard Medical Institutions.

Outcomes from an Early Adopters Home Hospital Accelerator for Acute Care at Home.

BACKGROUND: Following the Centers for Medicare and Medicaid Services' approval of the Acute Hospital Care at Home waiver, an increasing number of health care organizations launched Home Hospital (HH) programs in the USA. Ongoing barriers include access to HH expertise and a standard, comprehensive set of implementation tools. We created the HH Early Adopters Accelerator to bring together a network of health care organizations to develop tools ("knowledge products") necessary for HH implementation. OBJECTIVE: To demonstrate the feasibility of the Accelerator approach for generating and implementing relevant, high-quality knowledge products. DESIGN: Mixed methods evaluation of the Accelerator. Surveys and qualitative interviews of Accelerator participants were conducted. Surveys elicited feedback on the knowledge products, including time spent on development, perceived utility and quality, and implementation success. The qualitative interviews gathered more in-depth information on topics covered in the surveys. PARTICIPANTS: Eighteen healthcare organizations and 105 individuals participated in the Accelerator. KEY RESULTS: The Accelerator reached its goal and developed 20 knowledge products in 32 working weeks (more efficient than expected). Participants agreed that the knowledge products were useful (developers: 98.1%; stakeholders: 93.8%), of high quality (developers: 96.8%), and would improve patient care if implemented in their HH program (developers: 91.7%; stakeholders: 91.2%). Two thirds (66.7%) of the participating organizations who had implemented knowledge products at 3 months continued utilizing knowledge products in their HH program at 1 year. Agreement that knowledge products improve patient care persisted (92% strongly agreed or agreed) at 1 year. Several programs created new tools, policies, and workflows as a result of implementing the knowledge products. CONCLUSIONS: The Accelerator created high-quality, comprehensive knowledge products that healthcare organizations found useful for safe HH implementation 1 year later. The Accelerator approach can feasibly help healthcare organizations safely bridge the gap between innovation and standard practice.

Automation of Functional Mobility Assessments at Home Using a Multimodal Sensor System Integrating Inertial Measurement Units and Computer Vision (IMU-Vision).

OBJECTIVE: Functional movement assessments are routinely used to evaluate and track changes in mobility. The objective of this study was to evaluate a multimodal movement monitoring system developed for autonomous, home-based, functional movement assessment. METHODS: Fifty frail and prefrail adults were recruited from the Brigham and Women's Hospital at Home program to evaluate the feasibility and accuracy of applying the multimodal movement monitoring system to autonomously recognize and score functional activities collected in the home. Study subjects completed sit-to-stand, standing balance (Romberg, semitandem, and tandem), and walking test activities in likeness to the Short Physical Performance Battery. Test activities were identified and scored manually and by the multimodal movement monitoring system's activity recognition and scoring algorithms, which were previously trained on lab-based biomechanical data to integrate wearable inertial measurement unit (IMU) and external red-blue-green-depth vision data. Feasibility was quantified as the proportion of completed tests that were analyzable. Accuracy was quantified as the degree of agreement between the actual and system-identified activities. In an exploratory analysis of a subset of functional activity data, the accuracy of a preliminary activity-scoring algorithm was also evaluated. RESULTS: Activity recognition by the IMU-vision system had good feasibility and high accuracy. Of 271 test activities collected in the home, 217 (80%) were analyzable by the activity-recognition algorithm, which overall correctly identified 206 (95%) of the analyzable activities: 100% of walking, 97% of balance, and 82% of sit-to-stand activities (χ2(2) = 19.9). In the subset of 152 tests suitable for activity scoring, automatic and manual scores showed substantial agreement (Kw = 0.76 [0.69, 0.83]). CONCLUSIONS: Autonomous recognition and scoring of home-based functional activities is enabled by a multimodal movement monitoring system that integrates inertial measurement unit and vision data. Further algorithm training with ecologically valid data and a kitted system that is independently usable by patients are needed before fully autonomous, functional movement assessment is realizable. IMPACT: Functional movement assessments that can be administered in the home without a clinician present have the potential to democratize these evaluations and improve care access.

The diagnostic and triage accuracy of the GPT-3 artificial intelligence model: an observational study.

BACKGROUND: Artificial intelligence (AI) applications in health care have been effective in many areas of medicine, but they are often trained for a single task using labelled data, making deployment and generalisability challenging. How well a general-purpose AI language model performs diagnosis and triage relative to physicians and laypeople is not well understood. METHODS: We compared the predictive accuracy of Generative Pre-trained Transformer 3 (GPT-3)'s diagnostic and triage ability for 48 validated synthetic case vignettes (<50 words; sixth-grade reading level or below) of both common (eg, viral illness) and severe (eg, heart attack) conditions to a nationally representative sample of 5000 lay people from the USA who could use the internet to find the correct options and 21 practising physicians at Harvard Medical School. There were 12 vignettes for each of four triage categories: emergent, within one day, within 1 week, and self-care. The correct diagnosis and triage category (ie, ground truth) for each vignette was determined by two general internists at Harvard Medical School. For each vignette, human respondents and GPT-3 were prompted to list diagnoses in order of likelihood, and the vignette was marked as correct if the ground-truth diagnosis was in the top three of the listed diagnoses. For triage accuracy, we examined whether the human respondents' and GPT-3's selected triage was exactly correct according to the four triage categories, or matched a dichotomised triage variable (emergent or within 1 day vs within 1 week or self-care). We estimated GPT-3's diagnostic and triage confidence on a given vignette using a modified bootstrap resampling procedure, and examined how well calibrated GPT-3's confidence was by computing calibration curves and Brier scores. We also performed subgroup analysis by case acuity, and an error analysis for triage advice to characterise how its advice might affect patients using this tool to decide if they should seek medical care immediately. FINDINGS: Among all cases, GPT-3 replied with the correct diagnosis in its top three for 88% (42/48, 95% CI 75-94) of cases, compared with 54% (2700/5000, 53-55) for lay individuals (p<0.0001) and 96% (637/666, 94-97) for physicians (p=0·012). GPT-3 triaged 70% correct (34/48, 57-82) versus 74% (3706/5000, 73-75; p=0.60) for lay individuals and 91% (608/666, 89-93%; p<0.0001) for physicians. As measured by the Brier score, GPT-3 confidence in its top prediction was reasonably well calibrated for diagnosis (Brier score=0·18) and triage (Brier score=0·22). We observed an inverse relationship between case acuity and GPT-3 accuracy (p<0·0001) with a fitted trend line of -8·33% decrease in accuracy for every level of increase in case acuity. For triage error analysis, GPT-3 deprioritised truly emergent cases in seven instances. INTERPRETATION: A general-purpose AI language model without any content-specific training could perform diagnosis at levels close to, but below, physicians and better than lay individuals. We found that GPT-3's performance was inferior to physicians for triage, sometimes by a large margin, and its performance was closer to that of lay individuals. Although the diagnostic performance of GPT-3 was comparable to physicians, it was significantly better than a typical person using a search engine. FUNDING: The National Heart, Lung, and Blood Institute.

Hospital at home worldwide: Program and clinician characteristics from the World Hospital at Home Congress survey.

BACKGROUND: Hospital at home (HaH) delivers hospital-level care to acutely ill patients at home as a substitute for brick-and-mortar hospital care. The clinician and program characteristics of HaH programs worldwide are relatively unknown. We sought to describe the world's HaH clinicians and their programs' characteristics. METHODS: We analyzed a survey administered to all attendees of the 2023 World Hospital at Home Congress. Clinician characteristics included age, years worked in HaH, profession, burnout, and experience. Program characteristics included location, daily census, types of care delivery, and clinical capabilities. RESULTS: Of 670 attendees, about 305 were clinicians and 129 responded (42% response rate for clinicians). The majority of clinicians were 30-49 years old (65.1%), new to the field (70.5% worked less than 10 years), and part-time (18% dedicated >74% effort to HaH). Clinicians reported overall satisfaction with their job and low burnout. About half of programs were in Europe (52.1%), newly operational (44.7% less than 5 years), mostly operated in urban environments (87.2%), and mostly had a daily census of less than 25 patients (62.8%). Most programs operated 7-days per week (88.3%), performed intermittent or continuous remote monitoring (81.4%), used video communication (63.8%), and had some advanced capabilities such as in-home imaging (47.9%) and advanced procedures (23.4%). Visit frequencies to the patient's home were variable: most programs had physicians visit the home, nearly all had nurses visit the home, and fewer performed virtual visits. CONCLUSIONS: HaH clinicians and programs have significant similarities but also a fair number of divergent practices, much like brick-and-mortar hospital care. Further standardization of the care model will help to unify the field across the globe.

Hospital-Level Care at Home for Acutely Ill Adults in Rural Settings: Proof of Concept.

Residents in rural areas face barriers to accessing acute care. Rural home hospital (RHH) or delivery of acute care at home could represent an important clinical care model. This study assessed the feasibility and acceptability of RHH as a substitute to traditional hospital care. Patients were cared for by a remote RHH attending physician and an RHH registered nurse deployed to the home. The study team conducted daily check-ins with RHH clinicians to assess workflows for completion. Surveys assessed patient experience and qualitative interviews assessed perceived acceptability, safety, and quality of care. We completed qualitative analysis of the interviews and coded qualitative data into domains and subdomains through an iterative process. RHH was successfully deployed to three acutely ill patients in rural Utah. RHH admission, daily care, and discharge processes were accomplished for each patient. From qualitative analysis, we identified four domains: (1) Perceived comfort level during RHH admission, (2) Perceived safety during RHH admission, (3) Perceived quality of care during RHH admission, and (4) Perception of RHH workflows. We found acute care was delivered to rural homes with satisfactory patient and clinician experience. Team dynamics, technology build, robust clinical and operational workflows, and care coordination were important to a successful admission. Learnings from this study can inform program design and training for RHH teams and startup for larger RHH evaluation. Home hospital care is expanding rapidly in the United States and RHH could represent an important clinical care model.

Implementation of Agile in healthcare: methodology for a multisite home hospital accelerator.

BACKGROUND: The diffusion of innovation in healthcare is sluggish. Evidence-based care models and interventions take years to reach patients. We believe the healthcare community could deliver innovation to the bedside faster if it followed other sectors by employing an organisational framework for efficiently accomplishing work. Home hospital is an example of sluggish diffusion. This model provides hospital-level care in a patient's home instead of in a traditional hospital with equal or better outcomes. Home hospital uptake has steadily grown during the COVID-19 pandemic, yet barriers to launch remain for healthcare organisations, including access to expertise and implementation tools. The Home Hospital Early Adopters Accelerator was created to bring together a network of healthcare organisations to develop tools necessary for programme implementation. METHODS: The accelerator used the Agile framework known as Scrum to rapidly coordinate work across many different specialised skill sets and blend individuals who had no experience with one another into efficient teams. Its goal was to take 40 weeks to develop 20 'knowledge products',or tools critical to the development of a home hospital programme such as workflows, inclusion criteria and protocols. We conducted a mixed-methods evaluation of the accelerator's implementation, measuring teams' productivity and experience. RESULTS: 18 healthcare organisations participated in the accelerator to produce the expected 20 knowledge products in only 32 working weeks, a 20% reduction in time. Nearly all (97.4%) participants agreed or strongly agreed the Scrum teams worked well together, and 96.8% felt the teams produced a high-quality product. Participants consistently remarked that the Scrum team developed products much faster than their respective organisational teams. The accelerator was not a panacea: it was challenging for some participants to become familiar with the Scrum framework and some participants struggled with balancing participation in the Accelerator with their job duties. CONCLUSIONS: Implementation of an Agile-based accelerator that joined disparate healthcare organisations into teams equipped to create knowledge products for home hospitals proved both efficient and effective. We demonstrate that implementing an organisational framework to accomplish work is a valuable approach that may be transformative for the sector.

Patient Safety Indicators During the Initial COVID-19 Pandemic Surge in the United States.

OBJECTIVE: The COVID-19 pandemic presented a challenge to inpatient safety. It is unknown whether there were spillover effects due to COVID-19 into non-COVID-19 care and safety. We sought to evaluate the changes in inpatient Agency for Healthcare Research and Quality patient safety indicators (PSIs) in the United States before and during the first surge of the pandemic among patients admitted without COVID-19. METHODS: We analyzed trends in PSIs from January 2019 to June 2020 in patients without COVID-19 using data from IBM MarketScan Commercial Database. We included members of employer-sponsored or Medicare supplemental health plans with inpatient, non-COVID-19 admissions. The primary outcomes were risk-adjusted composite and individual PSIs. RESULTS: We analyzed 1,869,430 patients admitted without COVID-19. Among patients without COVID-19, the composite PSI score was not significantly different when comparing the first surge (Q2 2020) to the prepandemic period (e.g., Q2 2020 score of 2.46 [95% confidence interval {CI}, 2.34-2.58] versus Q1 2020 score of 2.37 [95% CI, 2.27-2.46]; P = 0.22). Individual PSIs for these patients during Q2 2020 were also not significantly different, except in-hospital fall with hip fracture (e.g., Q2 2020 was 3.42 [95% CI, 3.34-3.49] versus Q4 2019 was 2.45 [95% CI, 2.40-2.50]; P = 0.01). CONCLUSIONS: The first surge of COVID-19 was not associated with worse inpatient safety for patients without COVID-19, highlighting the ability of the healthcare system to respond to the initial surge of the pandemic.