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.

Implementation of a Continuous Patient Monitoring System in the Hospital Setting: A Qualitative Study.

BACKGROUND: Technology can improve care delivery, patient outcomes, and staff satisfaction, but integration into the clinical workflow remains challenging. To contribute to this knowledge area, this study examined the implementation continuum of a contact-free, continuous monitoring system (CFCM) in an inpatient setting. CFCM monitors vital signs and uses the information to alert clinicians of important changes, enabling early detection of patient deterioration. METHODS: Data were collected throughout the entire implementation continuum at a community teaching hospital. Throughout the study, 3 group and 24 individual interviews and five process observations were conducted. Postimplementation alarm response data were collected. Analysis was conducted using triangulation of information sources and two-coder consensus. RESULTS: Preimplementation perceived barriers were alarm fatigue, questions about accuracy and trust, impact on patient experience, and challenges to the status quo. Stakeholders identified the value of CFCM as preventing deterioration and benefitting patients who are not good candidates for telemetry. Educational materials addressed each barrier and emphasized the shared CFCM values. Mean alarm response times were below the desired target of two minutes. Postimplementation interview analysis themes revealed lessened concerns of alarm fatigue and improved trust in CFCM than anticipated. Postimplementation challenges included insufficient training for secondary users and impact on patient experience. CONCLUSION: In addition to understanding the preimplementation anticipated barriers to implementation and establishing shared value before implementation, future recommendations include studying strategies for optimal tailoring of education to each user group, identifying and reinforcing positive process changes after implementation, and including patient experience as the overarching element in frameworks for digital tool implementation.

Cost Savings Associated With Implementing 4 Total Joint Replacement Electronic Clinical Quality Measures Nationally: 2020-2040.

BACKGROUND: Digital transformation using widely available electronic data is a key component to improving health outcomes and customer choice and decreasing cost and measurement burden. Despite these benefits, existing information on the potential cost savings from electronic clinical quality measures (eCQMs) is limited. METHODS: We assessed the costs of implementing 4 eCQMs related to total hip and/or total knee arthroplasty into electronic health record systems across healthcare systems in the United States. We used published literature and technical expert panel consultation to calculate low-, mid-, and high-range hip and knee arthroplasty surgery projections, and used empirical testing, literature, and technical expert panel consultation to develop an economic model to assess projected cost savings of eCQMs when implemented nationally. RESULTS: Low-, mid-, and high-range projected cost savings for year's 2020, 2030, and 2040 were calculated for 4 orthopedic eCQMs. Mid-range projected cost savings for 2020 ranged from $7.9 to $31.9 million per measure per year. A breakeven of between 0.5% and 5.1% of adverse events (measure dependent) must be averted for cost savings to outweigh implementation costs. CONCLUSIONS: All measures demonstrated potential cost savings. These findings suggest that eCQMs have the potential to lower healthcare costs and improve patient outcomes without adding to physician documentation burden. The Centers for Medicare and Medicaid Services' investment in eCQMs is an opportunity to reduce adverse outcomes and excess costs in orthopedics.

Factors determining safety culture in hospitals: a scoping review.

OBJECTIVE: To evaluate and synthesise the factors determining patient safety culture in hospitals. METHODS: The scoping review protocol was based on the criteria of the Joanna Briggs Institute. Eligibility criteria were as follows: (1) empirical study published in a peer-reviewed journal; (2) used methods or tools to assess, study or measure safety culture or climate; (3) data collected in the hospital setting and (4) studies published in English. Relevant literature was located using PubMed, CINAHL, Web of Science and PsycINFO databases. Quantitative and qualitative analyses were performed using RStudio and the R interface for multidimensional analysis of texts and questionnaires (IRaMuTeQ). RESULTS: A total of 248 primary studies were included. The most used instruments for assessing safety culture were the Hospital Survey on Patient Safety Culture (n=104) and the Safety Attitudes Questionnaire (n=63). The Maslach Burnout Inventory (n=13) and Culture Assessment Scales based on patient perception (n=9) were used in association with cultural instruments. Sixty-six articles were included in the qualitative analysis. In word cloud and similarity analyses, the words 'communication' and 'leadership' were most prominent. Regarding the descending hierarchical classification analysis, the content was categorised into two main classes, one of which was subdivided into five subclasses: class 1a: job satisfaction and leadership (15.56%), class 1b: error response (22.22%), class 1c: psychological and empowerment nurses (20.00%), class 1d: trust culture (22.22%) and class 2: innovation worker (20.00%). CONCLUSION: The instruments presented elements that remained indispensable for assessing the safety culture, such as leadership commitment, open communication and learning from mistakes. There was also a tendency for research to assess patient and family engagement, psychological safety, nurses' engagement in decision-making and innovation.

Correction: Performance of a Web-Based Reference Database With Natural Language Searching Capabilities: Usability Evaluation of DynaMed and Micromedex With Watson.

[This corrects the article DOI: 10.2196/43960.].

Performance of a Web-Based Reference Database With Natural Language Searching Capabilities: Usability Evaluation of DynaMed and Micromedex With Watson.

BACKGROUND: Evidence-based point-of-care information (POCI) tools can facilitate patient safety and care by helping clinicians to answer disease state and drug information questions in less time and with less effort. However, these tools may also be visually challenging to navigate or lack the comprehensiveness needed to sufficiently address a medical issue. OBJECTIVE: This study aimed to collect clinicians' feedback and directly observe their use of the combined POCI tool DynaMed and Micromedex with Watson, now known as DynaMedex. EBSCO partnered with IBM Watson Health, now known as Merative, to develop the combined tool as a resource for clinicians. We aimed to identify areas for refinement based on participant feedback and examine participant perceptions to inform further development. METHODS: Participants (N=43) within varying clinical roles and specialties were recruited from Brigham and Women's Hospital and Massachusetts General Hospital in Boston, Massachusetts, United States, between August 10, 2021, and December 16, 2021, to take part in usability sessions aimed at evaluating the efficiency and effectiveness of, as well as satisfaction with, the DynaMed and Micromedex with Watson tool. Usability testing methods, including think aloud and observations of user behavior, were used to identify challenges regarding the combined tool. Data collection included measurements of time on task; task ease; satisfaction with the answer; posttest feedback on likes, dislikes, and perceived reliability of the tool; and interest in recommending the tool to a colleague. RESULTS: On a 7-point Likert scale, pharmacists rated ease (mean 5.98, SD 1.38) and satisfaction (mean 6.31, SD 1.34) with the combined POCI tool higher than the physicians, nurse practitioner, and physician's assistants (ease: mean 5.57, SD 1.64, and satisfaction: mean 5.82, SD 1.60). Pharmacists spent longer (mean 2 minutes, 26 seconds, SD 1 minute, 41 seconds) on average finding an answer to their question than the physicians, nurse practitioner, and physician's assistants (mean 1 minute, 40 seconds, SD 1 minute, 23 seconds). CONCLUSIONS: Overall, the tool performed well, but this usability evaluation identified multiple opportunities for improvement that would help inexperienced users.

The Safety of Inpatient Health Care.

BACKGROUND: Adverse events during hospitalization are a major cause of patient harm, as documented in the 1991 Harvard Medical Practice Study. Patient safety has changed substantially in the decades since that study was conducted, and a more current assessment of harm during hospitalization is warranted. METHODS: We conducted a retrospective cohort study to assess the frequency, preventability, and severity of patient harm in a random sample of admissions from 11 Massachusetts hospitals during the 2018 calendar year. The occurrence of adverse events was assessed with the use of a trigger method (identification of information in a medical record that was previously shown to be associated with adverse events) and from review of medical records. Trained nurses reviewed records and identified admissions with possible adverse events that were then adjudicated by physicians, who confirmed the presence and characteristics of the adverse events. RESULTS: In a random sample of 2809 admissions, we identified at least one adverse event in 23.6%. Among 978 adverse events, 222 (22.7%) were judged to be preventable and 316 (32.3%) had a severity level of serious (i.e., caused harm that resulted in substantial intervention or prolonged recovery) or higher. A preventable adverse event occurred in 191 (6.8%) of all admissions, and a preventable adverse event with a severity level of serious or higher occurred in 29 (1.0%). There were seven deaths, one of which was deemed to be preventable. Adverse drug events were the most common adverse events (accounting for 39.0% of all events), followed by surgical or other procedural events (30.4%), patient-care events (which were defined as events associated with nursing care, including falls and pressure ulcers) (15.0%), and health care-associated infections (11.9%). CONCLUSIONS: Adverse events were identified in nearly one in four admissions, and approximately one fourth of the events were preventable. These findings underscore the importance of patient safety and the need for continuing improvement. (Funded by the Controlled Risk Insurance Company and the Risk Management Foundation of the Harvard Medical Institutions.).

Testing of an Electronic Clinical Quality Measure for Diagnostic Delay of Venous Thromboembolism (DOVE) in Primary Care.

Venous Thromboembolism (VTE) is a serious, preventable public health problem that requires timely treatment. Because signs and symptoms are non-specific, patients often present to primary care providers with VTE symptoms prior to diagnosis. Today there are no federal measurement tools in place to track delayed diagnosis of VTE. We developed and tested an electronic clinical quality measure (eCQM) to quantify Diagnostic Delay of Venous Thromboembolism (DOVE); the rate of avoidable delayed VTE events occurring in patients with a VTE who had reported VTE symptoms in primary care within 30 days of diagnosis. DOVE uses routinely collected EHR data without contributing to documentation burden. DOVE was tested in two geographically distant healthcare systems. Overall DOVE rates were 72.60% (site 1) and 77.14% (site 2). This novel, data-driven eCQM could inform healthcare providers and facilities about opportunities to improve care, strengthen incentives for quality improvement, and ultimately improve patient safety.

Use of linked data to assess the impact of including out-of-hospital deaths on 30-day in-hospital mortality indicators: a retrospective cohort study.

BACKGROUND: The Canadian Institute for Health Information (CIHI) annually reports on health system performance indicators, including various 30-day in-hospital mortality rates. We aimed to assess the impact of including out-of-hospital deaths on 3 CIHI indicators: 30-day acute myocardial infarction (AMI) in-hospital mortality, 30-day stroke in-hospital mortality and hospital deaths following major surgery. METHODS: We followed national cohorts of patients admitted to hospital in 1 of 9 Canadian provinces for AMI, stroke and major surgery for 30-day all-cause mortality in 2 fiscal years (2011/12 and 2016/17). We calculated descriptive statistics to characterize the cohorts. The CIHI Discharge Abstract Database was linked with the Canadian Vital Statistics Death Database using a probabilistic algorithm to identify out-of-hospital deaths. We calculated absolute numbers, relative proportions and 30-day mortality rates for in-hospital, out-of-hospital and all deaths. We compared results between fiscal years. RESULTS: We found that hospital admissions increased between fiscal years for each indicator; however, cohort characteristics remained consistent. In 2016/17, the number of out-of-hospital deaths that occurred was 325 for AMI, 545 for stroke and 820 for major surgery. The relative proportions of out-of-hospital deaths ranged from 12.3% for AMI to 14.9% for major surgery in 2016/17 (an increase from 10.6% and 13.1%, respectively, from 2011/12). In-hospital mortality rates improved over time for all 3 indicators, while out-of-hospital mortality rates remained consistent between fiscal years at 0.8% for AMI, 1.9%-2.0% for stroke and 0.2%-0.3% for major surgery. INTERPRETATION: Improvements between fiscal years were attributable to reductions in in-hospital mortality, rather than deaths occurring outside of hospitals. Trends over time were the same for each indicator irrespective of whether in-hospital mortality or all deaths were measured.

Multi-Site Testing of an Opioid Prescribing Electronic Clinical Quality Measure Following Elective Primary Total Hip and/or Total Knee Arthroplasties.

As the United States faces the third wave of the ongoing opioid epidemic, development of measures which report on prolonged opioid prescribing (POP) rates, specifically following orthopedic surgeries, are needed to better understand and improve prescribing practices at the clinician group level. Brigham and Women's Hospital (BWH) has been contracted by the Centers for Medicare and Medicaid Services (CMS) to create a novel electronic clinical quality measure (eCQM) to quantify the prolonged opioid prescribing rate of opioid episodes lasting > 42 days in patients aged 18+ years following elective primary total hip arthroplasties (THA) and/or total knee arthroplasties (TKA) for use in the Merit-Based Incentive Payment System (MIPS). When this measure was tested on two geographically distinct sites, it was found that the THA rate was 3.80% and 16.07% at sites 1 and 2, respectively, and that the TKA rate is 7.65% and 24.15% at sites 1 and 2, respectively. This manuscript reports on the testing of this eCQM between these two sites, highlighting differences in state and organizational level policies regarding opioid prescribing and documentation practices.

Testing a Novel Inpatient Respiratory Depression Electronic Clinical Quality Measure (eCQM) for Orthopedic Practice in Two Large U.S. Health Systems.

The objective of this study was to assess the feasibility of using an electronic clinical quality measure (eCQM) to assess inpatient respiratory depression rates following elective primary total hip or total knee arthroplasty using data routinely collected in electronic health records. Measure testing was conducted at two large urban, academic health systems - Mass General Brigham and a geographically distant system in southern U.S. The risk-adjusted inpatient respiratory depression rates were 3.83 and 2.73% for the two health systems, respectively. Clinician group rates ranged from 1.40 to 4.35%, demonstrating opportunity for improvement. Both the data and measure specifications showed strong reliability and validity to allow for calculation of accurate and comparable rates of inpatient respiratory depression.

Intelligent Telehealth in Pharmacovigilance: A Future Perspective.

Pharmacovigilance improves patient safety by detecting and preventing adverse drug events. However, challenges exist that limit adverse drug event detection, resulting in many adverse drug events being underreported or inaccurately reported. One challenge includes having access to large data sets from various sources including electronic health records and wearable medical devices. Artificial intelligence, including machine learning methods, such as natural language processing and deep learning, can detect and extract information about adverse drug events, thus automating the pharmacovigilance process and improving the surveillance of known and documented adverse drug events. In addition, with the increased demand for telehealth services, for managing both acute and chronic diseases, artificial intelligence methods can play a role in detecting and preventing adverse drug events. In this review, we discuss two use cases of how artificial intelligence methods may be useful to improve the quality of pharmacovigilance and the role of artificial intelligence in telehealth practices.

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review.

Adverse drug events (ADEs) represent one of the most prevalent types of health-care-related harm, and there is substantial room for improvement in the way that they are currently predicted and detected. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged to reduce the frequency of ADEs. We focused on modern machine learning techniques and natural language processing. 78 articles were included in the scoping review. Studies were heterogeneous and applied various AI techniques covering a wide range of medications and ADEs. We identified several key use cases in which AI could contribute to reducing the frequency and consequences of ADEs, through prediction to prevent ADEs and early detection to mitigate the effects. Most studies (73 [94%] of 78) assessed technical algorithm performance, and few studies evaluated the use of AI in clinical settings. Most articles (58 [74%] of 78) were published within the past 5 years, highlighting an emerging area of study. Availability of new types of data, such as genetic information, and access to unstructured clinical notes might further advance the field.

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases.

Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

The potential of artificial intelligence to improve patient safety: a scoping review.

Artificial intelligence (AI) represents a valuable tool that could be used to improve the safety of care. Major adverse events in healthcare include: healthcare-associated infections, adverse drug events, venous thromboembolism, surgical complications, pressure ulcers, falls, decompensation, and diagnostic errors. The objective of this scoping review was to summarize the relevant literature and evaluate the potential of AI to improve patient safety in these eight harm domains. A structured search was used to query MEDLINE for relevant articles. The scoping review identified studies that described the application of AI for prediction, prevention, or early detection of adverse events in each of the harm domains. The AI literature was narratively synthesized for each domain, and findings were considered in the context of incidence, cost, and preventability to make projections about the likelihood of AI improving safety. Three-hundred and ninety-two studies were included in the scoping review. The literature provided numerous examples of how AI has been applied within each of the eight harm domains using various techniques. The most common novel data were collected using different types of sensing technologies: vital sign monitoring, wearables, pressure sensors, and computer vision. There are significant opportunities to leverage AI and novel data sources to reduce the frequency of harm across all domains. We expect AI to have the greatest impact in areas where current strategies are not effective, and integration and complex analysis of novel, unstructured data are necessary to make accurate predictions; this applies specifically to adverse drug events, decompensation, and diagnostic errors.

Testing of a Risk-Standardized Complication Rate Electronic Clinical Quality Measure (eCQM) for Total Hip and/or Total Knee Arthroplasty.

Supported by the Centers for Medicare & Medicaid Services (CMS), Brigham and Women's Hospital (BWH) has retooled the existing claims-based measures NQF1550 and NQF3493 into an electronic clinical quality measure (eCQM) to assess the risk-standardized complication rate (RSCR) following elective primary total hip (THA) and knee arthroplasty (TKA) at the clinician group level. This novel eCQM includes risk-adjustment for social determinants of health, includes all adult patients from all payers, leverages electronic health records (EHRs) rather than claims-based data, and includes both inpatient and outpatient procedures and complications which offers benefits compared to existing metrics. Following testing in two geographically different healthcare systems, the overall risk-standardized complication rate within 90 days following THA and TKA at the two sites was 3.60% (Site 1) and 3.70% (Site 2). This measure is designed for use in the Merit-Based Incentive Payment System (MIPS).

Testing of a Risk-Standardized Major Bleeding and Venous Thromboembolism Electronic Clinical Quality Measure for Elective Total Hip and/or Knee Arthroplasties.

Brigham and Women's Hospital has received funding from the Centers for Medicare and Medicaid Services to develop a novel electronic clinical quality measure to assess the risk-standardized major bleeding and venous thromboembolism (VTE) rate following elective total hip and/or knee arthroplasty. There are currently no existing measures that evaluate both the bleeding and VTE events following joint arthroplasty (TJA). Our novel composite measure was tested within two academic health systems with 17 clinician groups meeting the inclusion criteria. Following risk adjustment, the overall adjusted bleeding rate was 3.87% and ranged between 1.99% - 5.66%. The unadjusted VTE rate was 0.39% and ranged between 0% - 2.65%. The overall VTE/Bleeding composite score was 2.15 and ranged between 1.15 - 3.19. This measure seeks to provide clinician groups with a tool to assess their patient bleeding and VTE rates and compare them to their peers, ultimately providing an evidence-based quality metric assessing orthopedic practices.