Development and Implementation of a Digital Quality Measure of Emergency Cancer Diagnosis.

PURPOSE: Missed and delayed cancer diagnoses are common, harmful, and often preventable. Automated measures of quality of cancer diagnosis are lacking but could identify gaps and guide interventions. We developed and implemented a digital quality measure (dQM) of cancer emergency presentation (EP) using electronic health record databases of two health systems and characterized the measure's association with missed opportunities for diagnosis (MODs) and mortality. METHODS: On the basis of literature and expert input, we defined EP as a new cancer diagnosis within 30 days after emergency department or inpatient visit. We identified EPs for lung cancer and colorectal cancer (CRC) in the Department of Veterans Affairs (VA) and Geisinger from 2016 to 2020. We validated measure accuracy and identified preceding MODs through standardized chart review of 100 records per cancer per health system. Using VA's longitudinal encounter and mortality data, we applied logistic regression to assess EP's association with 1-year mortality, adjusting for cancer stage and demographics. RESULTS: Among 38,565 and 2,914 patients with lung cancer and 14,674 and 1,649 patients with CRCs at VA and Geisinger, respectively, our dQM identified EPs in 20.9% and 9.4% of lung cancers, and 22.4% and 7.5% of CRCs. Chart reviews revealed high positive predictive values for EPs across sites and cancer types (72%-90%), and a substantial percent represented MODs (48.8%-84.9%). EP was associated with significantly higher odds of 1-year mortality for lung cancer and CRC (adjusted odds ratio, 1.78 and 1.83, respectively, 95% CI, 1.63 to 1.86 and 1.61 to 2.07). CONCLUSION: A dQM for cancer EP was strongly associated with both mortality and MODs. The findings suggest a promising automated approach to measuring quality of cancer diagnosis in US health systems.

An electronic trigger to detect telemedicine-related diagnostic errors.

INTRODUCTION: The COVID-19 pandemic advanced the use of telehealth-facilitated care. However, little is known about how to measure safety of clinical diagnosis made through telehealth-facilitated primary care. METHODS: We used the seven-step Safer Dx Trigger Tool framework to develop an electronic trigger (e-trigger) tool to identify potential missed opportunities for more timely diagnosis during primary care telehealth visits at a large Department of Veterans Affairs facility. We then applied the e-trigger algorithm to electronic health record data related to primary care visits during a 1-year period (1 April 2020-31 March 2021). The algorithm identified patients with unexpected visits within 10 days of an index telemedicine visit and classified such records as e-trigger positive. We then validated the e-trigger's ability to detect missed opportunities in diagnosis using chart reviews based on a structured data collection instrument (the Revised Safer Dx instrument). RESULTS: We identified 128,761 telehealth visits (32,459 unique patients), of which 434 visits led to subsequent unplanned emergency department (ED), hospital, or primary care visits within 10 days of the index visit. Of these, 116 were excluded for clinical reasons (trauma, injury, or childbirth), leaving 318 visits (240 unique patients) needing further evaluation. From these, 100 records were randomly selected for review, of which four were falsely flagged due to invalid data (visits by non-providers or those incorrectly flagged as completed telehealth visits). Eleven patients had a missed opportunity in diagnosis, yielding a positive predictive value of 11%. DISCUSSION: Electronic triggers that identify missed opportunities for additional evaluation could help advance the understanding of safety of clinical diagnosis made in telehealth-enabled care. Better measurement can help determine which patients can safely be cared for via telemedicine versus traditional in-person visits.

Medical Home Implementation and Follow-Up of Cancer-Related Abnormal Test Results in the Veterans Health Administration.

Importance: Lack of timely follow-up of cancer-related abnormal test results can lead to delayed or missed diagnoses, adverse cancer outcomes, and substantial cost burden for patients. Care delivery models, such as the Veterans Affairs' (VA) Patient-Aligned Care Team (PACT), which aim to improve patient-centered care coordination, could potentially also improve timely follow-up of abnormal test results. PACT was implemented nationally in the VA between 2010 and 2012. Objective: To evaluate the long-term association between PACT implementation and timely follow-up of abnormal test results related to the diagnosis of 5 different cancers. Design, Setting, and Participants: This multiyear retrospective cohort study used 14 years of VA data (2006-2019), which were analyzed using panel data-based random-effects linear regressions. The setting included all VA clinics and facilities. The participants were adult patients who underwent diagnostic testing related to 5 different cancers and had abnormal test results. Data extraction and statistical analyses were performed from September 2021 to December 2023. Exposure: Calendar years denoting preperiods and postperiods of PACT implementation, and the PACT Implementation Progress Index Score denoting the extent of implementation in each VA clinic and facility. Main Outcome and Measure: Percentage of potentially missed timely follow-ups of abnormal test results. Results: This study analyzed 6 data sets representing 5 different types of cancers. During the initial years of PACT implementation (2010 to 2013), percentage of potentially missed timely follow-ups decreased between 3 to 7 percentage points for urinalysis suggestive of bladder cancer, 12 to 14 percentage points for mammograms suggestive of breast cancer, 19 to 22 percentage points for fecal tests suggestive of colorectal cancer, and 6 to 13 percentage points for iron deficiency anemia laboratory tests suggestive of colorectal cancer, with no statistically significant changes for α-fetoprotien tests and lung cancer imaging. However, these beneficial reductions were not sustained over time. Better PACT implementation scores were associated with a decrease in potentially missed timely follow-up percentages for urinalysis (0.3-percentage point reduction [95% CI, -0.6 to -0.1] with 1-point increase in the score), and laboratory tests suggestive of iron deficiency anemia (0.5-percentage point reduction [95% CI,-0.8 to -0.2] with 1-point increase in the score). Conclusions and Relevance: This cohort study found that implementation of PACT in the VA was associated with a potential short-term improvement in the quality of follow-up for certain test results. Additional multifaceted sustained interventions to reduce missed test results are required to prevent care delays.

Study protocol: maintaining preventive care during public health emergencies through effective coordination.

BACKGROUND: Screening lies at the heart of preventive care. However, COVID-19 dramatically disrupted routine screening efforts, resulting in excess mortality not directly attributable to COVID-19. Screening rates during COVID varied markedly by facility and clinical condition, suggesting susceptibilities in screening and referral process workflow. To better understand these susceptibilities and identify new practices to mitigate interrupted care, we propose a qualitative study comparing facilities that exhibited high, low, and highly variable performance (respectively) in screening rates before and during the pandemic. We will be guided by Weaver et al.'s multi-team systems (MTS) model of coordination, using cancer and mental health screening rates as exemplars. METHOD: Qualitative analysis of interviews and focus groups with primary care personnel, leadership, and patients at 10 VA medical centers. We will select sites based on rurality, COVID-19 caseload at the beginning of the pandemic, and performance on five outpatient clinical performance indicators of cancer and mental health screening. Sites will be categorized into one of five screening performance groups: high performers, low performers, improvers, plummeters, and highly variable. We will create process maps for each performance measure to create a workflow baseline and then interview primary care leadership to update the map at each site. We will clinician conduct focus groups to elicit themes regarding clinician coordination patterns (e.g., handoffs), strategies, and barriers/facilitators to screening during COVID. We will also conduct patient interviews to examine their screening experience during this period, for context. All interviews and focus groups will be audio-recorded, transcribed, and enhanced by field notes. We will analyze clinician transcripts and field notes using iterative, rapid analysis. Patient interviews will be analyzed using inductive/deductive content analysis. DISCUSSION: Our study represents a unique opportunity to inform the multi-team systems literature by identifying specific forms of information exchange, collective problem solving, and decision-making associated with higher and improved clinical performance. Specifically, our study aims to detect the specific points in the screening and referral process most susceptible to disruption and coordination processes that, if changed, will yield the highest value. Findings apply to future pandemics or any event with the potential to disrupt care.

Developing electronic clinical quality measures to assess the cancer diagnostic process.

OBJECTIVE: Measures of diagnostic performance in cancer are underdeveloped. Electronic clinical quality measures (eCQMs) to assess quality of cancer diagnosis could help quantify and improve diagnostic performance. MATERIALS AND METHODS: We developed 2 eCQMs to assess diagnostic evaluation of red-flag clinical findings for colorectal (CRC; based on abnormal stool-based cancer screening tests or labs suggestive of iron deficiency anemia) and lung (abnormal chest imaging) cancer. The 2 eCQMs quantified rates of red-flag follow-up in CRC and lung cancer using electronic health record data repositories at 2 large healthcare systems. Each measure used clinical data to identify abnormal results, evidence of appropriate follow-up, and exclusions that signified follow-up was unnecessary. Clinicians reviewed 100 positive and 20 negative randomly selected records for each eCQM at each site to validate accuracy and categorized missed opportunities related to system, provider, or patient factors. RESULTS: We implemented the CRC eCQM at both sites, while the lung cancer eCQM was only implemented at the VA due to lack of structured data indicating level of cancer suspicion on most chest imaging results at Geisinger. For the CRC eCQM, the rate of appropriate follow-up was 36.0% (26 746/74 314 patients) in the VA after removing clinical exclusions and 41.1% at Geisinger (1009/2461 patients; P < .001). Similarly, the rate of appropriate evaluation for lung cancer in the VA was 61.5% (25 166/40 924 patients). Reviewers most frequently attributed missed opportunities at both sites to provider factors (84 of 157). CONCLUSIONS: We implemented 2 eCQMs to evaluate the diagnostic process in cancer at 2 large health systems. Health care organizations can use these eCQMs to monitor diagnostic performance related to cancer.

Electronic Health Records' Support for Primary Care Physicians' Situation Awareness: A Metanarrative Review.

OBJECTIVE: Situation awareness (SA) refers to people's perception and understanding of their dynamic environment. In primary care, reduced SA among physicians increases errors in clinical decision-making and, correspondingly, patients' risk of experiencing adverse outcomes. Our objective was to understand the extent to which electronic health records (EHRs) support primary care physicians (PCPs)' SA during clinical decision-making. METHOD: We conducted a metanarrative review of papers in selected academic databases, including CINAHL and MEDLINE. Eligible studies included original peer-reviewed research published between January 2012 and August 2020 on PCP-EHR interactions. We iteratively queried, screened, and summarized literature focused on EHRs supporting PCPs' clinical decision-making and care management for adults. Then, we mapped findings to an established SA framework to classify external factors (individual, task, and system) affecting PCPs' levels of SA (1-Perception, 2-Comprehension, and 3-Projection) and identified SA barriers. RESULTS: From 1504 articles identified, we included and synthesized 19 studies. Study designs were largely noninterventional. Studies described EHR workflow misalignments, usability issues, and communication challenges. EHR information, including lab results and care plans, was characterized as incomplete, untimely, or irrelevant. Unmet information needs made it difficult for PCPs to obtain even basic SA, Level 1 SA. Prevalent barriers to PCPs developing SA with EHRs were errant mental models, attentional tunneling, and data overload. CONCLUSION: Based on our review, EHRs do not support the development of higher levels of SA among PCPs. Review findings suggest SA-oriented design processes for health information technology could improve PCPs' SA, satisfaction, and decision-making.

Assessing the Cost-effectiveness of a Hypothetical Disease-modifying Therapy With Limited Duration for the Treatment of Early Symptomatic Alzheimer Disease.

PURPOSE: Clinical trials have produced promising results for disease-modifying therapies (DMTs) for Alzheimer's disease (AD); however, the evidence on their potential cost-effectiveness is limited. This study assesses the cost-effectiveness of a hypothetical DMT with a limited treatment duration in AD. METHODS: We developed a Markov state-transition model to estimate the cost-effectiveness of a hypothetical DMT plus best supportive care (BSC) versus BSC alone among Americans living with mild cognitive impairment (MCI) due to AD or mild AD. AD states included MCI due to AD, mild AD, moderate AD, severe AD, and death. A hypothetical DMT was assumed to confer a 30% reduction in progression from MCI and mild AD. The base case annual drug acquisition cost was assumed to be $56,000. Other medical and indirect costs were obtained from published literature or list prices. Utilities for patients and caregivers were obtained from the published literature and varied by AD state and care setting (community care or long-term care). We considered 3 DMT treatment strategies: (1) treatment administered until patients reached severe AD (continuous strategy), (2) treatment administered for a maximum duration of 18 months or when patients reached severe AD (fixed-duration strategy), and (3) 40% of patients discontinuing treatment at 6 months because of amyloid plaque clearance and the remaining patients continuing treatment until 18 months or until they reached severe AD (test-and-discontinue strategy). Incremental cost-effectiveness ratios (ICERs) were calculated as the incremental cost per quality-adjusted life-year (QALY) gained. FINDINGS: From the health care sector perspective, continuous treatment with a hypothetical DMT versus BSC resulted in an ICER of $612,354 per QALY gained. The ICER decreased to $157,288 per QALY gained in the fixed-duration strategy, driven by large reductions in treatment costs. With 40% of patients discontinuing treatment at 6 months (test-and-discontinue strategy), the ICER was $125,631 per QALY gained. In sensitivity and scenario analyses, the ICER was the most sensitive to changes in treatment efficacy, treatment cost, and the initial population AD state distribution. From the modified societal perspective, ICERs were 6.3%, 20.4%, and 25.1% lower than those from the health care sector perspective for the continuous, fixed-duration, and test-and-discontinue strategies, respectively. IMPLICATIONS: Under a set of assumptions for annual treatment costs and the magnitude and duration of treatment efficacy, DMTs used for a limited duration may deliver value consistent with accepted US cost-effectiveness thresholds.

Cancer Evaluations During the COVID-19 Pandemic: An Observational Study Using National Veterans Affairs Data.

INTRODUCTION: Fewer cancer diagnoses have been made during the COVID-19 pandemic. Pandemic-related delays in cancer diagnosis could occur from limited access to care or patient evaluation delays (e.g., delayed testing after abnormal results). Follow-up of abnormal test results warranting evaluation for cancer was examined before and during the pandemic. METHODS: Electronic trigger algorithms were applied to the Department of Veterans Affairs electronic health record data to assess follow-up of abnormal test results before (March 10, 2019-March 7, 2020) and during (March 8, 2020-March 6, 2021) the pandemic. RESULTS: Electronic triggers were applied to 8,021,406 veterans' electronic health records to identify follow-up delays for abnormal results warranting evaluation for 5 cancers: bladder (urinalysis with high-grade hematuria), breast (abnormal mammograms), colorectal (positive fecal occult blood tests/fecal immunochemical tests or results consistent with iron deficiency anemia), liver (elevated alpha-fetoprotein), and lung (chest imaging suggestive of malignancy) cancers. Between prepandemic and pandemic periods, test quantities decreased by 12.6%-27.8%, and proportions of abnormal results lacking follow-up decreased for urinalyses (-0.8%), increased for fecal occult blood tests/fecal immunochemical test (+2.3%) and chest imaging (+1.8%), and remained constant for others. Follow-up times decreased for most tests; however, control charts suggested increased delays at 2 stages: early (pandemic beginning) for urinalyses, mammograms, fecal occult blood tests/fecal immunochemical test, iron deficiency anemia, and chest imaging and late (30-45 weeks into pandemic) for mammograms, fecal occult blood tests/fecal immunochemical test, and iron deficiency anemia. CONCLUSIONS: Although early pandemic delays in follow-up may have led to reduced cancer rates, the significant decrease in tests performed is likely a large driver of these reductions. Future emergency preparedness efforts should bolster essential follow-up and testing procedures to facilitate timely cancer diagnosis.

Translating electronic health record-based patient safety algorithms from research to clinical practice at multiple sites.

INTRODUCTION: Researchers are increasingly developing algorithms that impact patient care, but algorithms must also be implemented in practice to improve quality and safety. OBJECTIVE: We worked with clinical operations personnel at two US health systems to implement algorithms to proactively identify patients without timely follow-up of abnormal test results that warrant diagnostic evaluation for colorectal or lung cancer. We summarise the steps involved and lessons learned. METHODS: Twelve sites were involved across two health systems. Implementation involved extensive software documentation, frequent communication with sites and local validation of results. Additionally, we used automated edits of existing code to adapt it to sites' local contexts. RESULTS: All sites successfully implemented the algorithms. Automated edits saved sites significant work in direct code modification. Documentation and communication of changes further aided sites in implementation. CONCLUSION: Patient safety algorithms developed in research projects were implemented at multiple sites to monitor for missed diagnostic opportunities. Automated algorithm translation procedures can produce more consistent results across sites.

An evidence-based, structured, expert approach to selecting essential indicators of primary care quality.

BACKGROUND: The purpose of this article is to illustrate the application of an evidence-based, structured performance measurement methodology to identify, prioritize, and (when appropriate) generate new measures of health care quality, using primary care as a case example. Primary health care is central to the health care system and health of the American public; thus, ensuring high quality is essential. Due to its complexity, ensuring high-quality primary care requires measurement frameworks that can assess the quality of the infrastructure, workforce configurations, and processes available. This paper describes the use of the Productivity Measurement and Enhancement System (ProMES) to compile a targeted set of such measures, prioritized according to their contribution and value to primary care. METHODS: We adapted ProMES to select and rank existing primary care measures according to value to the primary care clinic. Nine subject matter experts (SMEs) consisting of clinicians, hospital leaders and national policymakers participated in facilitated expert elicitation sessions to identify objectives of performance, corresponding measures, and priority rankings. RESULTS: The SMEs identified three fundamental objectives: access, patient-health care team partnerships, and technical quality. The SMEs also selected sixteen performance indicators from the 44 pre-vetted, currently existing measures from three different data sources for primary care. One indicator, Team 2-Day Post Discharge Contact Ratio, was selected as an indicator of both team partnerships and technical quality. Indicators were prioritized according to value using the contingency functions developed by the SMEs. CONCLUSION: Our article provides an actionable guide to applying ProMES, which can be adapted to the needs of various industries, including measure selection and modification from existing data sources, and proposing new measures. Future work should address both logistical considerations (e.g., data capture, common data/programming language) and lingering measurement challenges, such as operationalizating measures to be meaningful and interpretable across health care settings.

Why Test Results Are Still Getting "Lost" to Follow-up: a Qualitative Study of Implementation Gaps.

BACKGROUND: Lack of timely follow-up of abnormal test results is common and has been implicated in missed or delayed diagnosis, resulting in potential for patient harm. OBJECTIVE: As part of a larger project to implement change strategies to improve follow-up of diagnostic test results, this study sought to identify specifically where implementation gaps exist, as well as possible solutions identified by front-line staff. DESIGN: We used a semi-structured interview guide to collect qualitative data from Veterans Affairs (VA) facility staff who had experience with test results management and patient safety. SETTING: Twelve VA facilities across the USA. PARTICIPANTS: Facility staff members (n = 27), including clinicians, lab and imaging professionals, nursing staff, patient safety professionals, and leadership. APPROACH: We conducted a content analysis of interview transcripts to identify perceived barriers and high-risk areas for effective test result management, as well as recommendations for improvement. RESULTS: We identified seven themes to guide further development of interventions to improve test result follow-up. Themes related to trainees, incidental findings, tracking systems for electronic health record notifications, outdated contact information, referrals, backup or covering providers, and responsibility for test results pending at discharge. Participants provided recommendations for improvement within each theme. CONCLUSIONS: Perceived barriers and recommendations for improving test result follow-up often reflected previously known problems and their corresponding solutions, which have not been consistently implemented in practice. Better policy solutions and improvement methods, such as quality improvement collaboratives, may bridge the implementation gaps between knowledge and practice.

Dashboards for visual display of patient safety data: a systematic review.

BACKGROUND: Methods to visualise patient safety data can support effective monitoring of safety events and discovery of trends. While quality dashboards are common, use and impact of dashboards to visualise patient safety event data remains poorly understood. OBJECTIVES: To understand development, use and direct or indirect impacts of patient safety dashboards. METHODS: We conducted a systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched PubMed, EMBASE and CINAHL for publications between 1 January 1950 and 30 August 2018 involving use of dashboards to display data related to safety targets defined by the Agency for Healthcare Research and Quality's Patient Safety Net. Two reviewers independently reviewed search results for inclusion in analysis and resolved disagreements by consensus. We collected data on development, use and impact via standardised data collection forms and analysed data using descriptive statistics. RESULTS: Literature search identified 4624 results which were narrowed to 33 publications after applying inclusion and exclusion criteria and consensus across reviewers. Publications included only time series and case study designs and were inpatient focused and emergency department focused. Information on direct impact of dashboards was limited, and only four studies included informatics or human factors principles in development or postimplementation evaluation. DISCUSSION: Use of patient-safety dashboards has grown over the past 15 years, but impact remains poorly understood. Dashboard design processes rarely use informatics or human factors principles to ensure that the available content and navigation assists task completion, communication or decision making. CONCLUSION: Design and usability evaluation of patient safety dashboards should incorporate informatics and human factors principles. Future assessments should also rigorously explore their potential to support patient safety monitoring including direct or indirect impact on patient safety.

Implementing Health Care Quality Measures in Electronic Health Records: A Conceptual Model.

BACKGROUND/OBJECTIVES: There is significant literature on the development and validation of quality measures, but comparably less on their implementation into learning health systems. Electronic Health Records (EHRs) have made vast amounts of data available for quality improvement purposes. In this paper we describe a conceptual model for EHR implementation of quality measures. DESIGN: The model involves five steps: (1) select a measure; (2) define measure criteria; (3) validate criteria and measurement process; (4) improve recording of measure-related activity; and (5) engage quality improvement processes. The model was used to develop and implement a quality measure in the Home-Based Medical Care (HBMC) setting. SETTING: Harris Health House Call Program (HHHC) provides primary medical and palliative care for homebound patients in Houston. PARTICIPANTS: Four-hundred twenty-four primary care patients followed in the HHHC. MEASUREMENT: Completion rate of the 9-item Patient Health Questionnaire (PHQ-9) within the Electronic Health Record of newly enrolled HHHC patients. RESULTS: Use of the conceptual model to guide implementation of a quality measure of depression screening in a HMBC practice was successful. Additional components of early leadership and clinician buy-in were required, as well as strong relationships with IT to ease implementation and limit disruptions in clinicians' work-flow. CONCLUSION: This conceptual model was feasible for guiding implementation of a quality measure for depression care of HBMC patients, and it can guide broader implementation of EHR-based quality measures in the future.

Application of Human Factors Methods to Understand Missed Follow-up of Abnormal Test Results.

OBJECTIVE: This study demonstrates application of human factors methods for understanding causes for lack of timely follow-up of abnormal test results ("missed results") in outpatient settings. METHODS: We identified 30 cases of missed test results by querying electronic health record data, developed a critical decision method (CDM)-based interview guide to understand decision-making processes, and interviewed physicians who ordered these tests. We analyzed transcribed responses using a contextual inquiry (CI)-based methodology to identify contextual factors contributing to missed results. We then developed a CI-based flow model and conducted a fault tree analysis (FTA) to identify hierarchical relationships between factors that delayed action. RESULTS: The flow model highlighted barriers in information flow and decision making, and the hierarchical model identified relationships between contributing factors for delayed action. Key findings including underdeveloped methods to track follow-up, as well as mismatches, in communication channels, timeframes, and expectations between patients and physicians. CONCLUSION: This case report illustrates how human factors-based approaches can enable analysis of contributing factors that lead to missed results, thus informing development of preventive strategies to address them.

The Effect of Laboratory Test-Based Clinical Decision Support Tools on Medication Errors and Adverse Drug Events: A Laboratory Medicine Best Practices Systematic Review.

BACKGROUND: Laboratory and medication data in electronic health records create opportunities for clinical decision support (CDS) tools to improve medication dosing, laboratory monitoring, and detection of side effects. This systematic review evaluates the effectiveness of such tools in preventing medication-related harm. METHODS: We followed the Laboratory Medicine Best Practice (LMBP) initiative's A-6 methodology. Searches of 6 bibliographic databases retrieved 8508 abstracts. Fifteen articles examined the effect of CDS tools on (a) appropriate dose or medication (n = 5), (b) laboratory monitoring (n = 4), (c) compliance with guidelines (n = 2), and (d) adverse drug events (n = 5). We conducted meta-analyses by using random-effects modeling. RESULTS: We found moderate and consistent evidence that CDS tools applied at medication ordering or dispensing can increase prescriptions of appropriate medications or dosages [6 results, pooled risk ratio (RR), 1.48; 95% CI, 1.27-1.74]. CDS tools also improve receipt of recommended laboratory monitoring and appropriate treatment in response to abnormal test results (6 results, pooled RR, 1.40; 95% CI, 1.05-1.87). The evidence that CDS tools reduced adverse drug events was inconsistent (5 results, pooled RR, 0.69; 95% CI, 0.46-1.03). CONCLUSIONS: The findings support the practice of healthcare systems with the technological capability incorporating test-based CDS tools into their computerized physician ordering systems to (a) identify and flag prescription orders of inappropriate dose or medications at the time of ordering or dispensing and (b) alert providers to missing laboratory tests for medication monitoring or results that warrant a change in treatment. More research is needed to determine the ability of these tools to prevent adverse drug events.

An Exploration of Barriers, Facilitators, and Suggestions for Improving Electronic Health Record Inbox-Related Usability: A Qualitative Analysis.

Importance: Managing messages in the electronic health record (EHR) inbox consumes substantial amounts of physician time. Certain factors associated with inbox management, such as poor usability and excessive and unnecessary inbox messages, have been associated with physician burnout. Additionally, inbox design, usability, and workflows are associated with physicians' situational awareness (ie, perception, comprehension, and projection of clinical status) and efficiency of processing EHR inbox messages. Understanding factors associated with inbox usability could improve future EHR inbox designs and workflows, thus reducing risk of burnout while improving patient safety. Objective: To determine barriers, facilitators, and suggestions associated with EHR inbox-related usability. Design, Setting, and Participants: This qualitative study included cognitive walkthroughs of EHR inbox management with 25 physicians (17 primary care physicians and 8 specialists) at 6 large health care organizations using 4 different EHR systems between May 6, 2015, and September 19, 2016. While processing EHR inbox messages, participants identified facilitators and barriers associated with EHR inbox situational awareness and processing efficiency and potential interventions to address such barriers. A qualitative analysis was performed on transcribed recordings using an inductive thematic approach with an 8-dimension sociotechnical model as a theoretical lens from May 6, 2015, to August 15, 2019. Results: The cognitive walkthroughs identified 60 barriers, 32 facilitators, and 28 suggestions for improving the EHR inbox. Emergent data fit within 5 major themes: message processing complexity, inbox interface design, cognitive load, team communication, and inbox message content. Within these themes, similar barriers were identified across sites, such as poor usability due the high numbers of clicks needed to accomplish actions. In certain instances, an identified facilitator at one site provided the exact solution needed to address a barrier identified at another site. Conclusions and Relevance: This qualitative study found that usability of the EHR inbox is often suboptimal and variable across sites, suggesting lack of shared best practices related to information management. Implementation of optimized design features and workflows will require EHR developers and health care organizations to collectively share this responsibility. Development of regional or national consortia to support collaborative sharing and implementation of EHR system best practices across EHR developers and health care organizations could also improve safety and efficiency and reduce physician burnout.

A Proof of Concept Study Demonstrating the Feasibility of a Text Message Prompt for Hepatitis C Testing in the 1945-1965 Birth Cohort.

Purpose Despite national recommendations stating all individuals in the 1945-1965 "birth cohort" be tested for hepatitis C virus (HCV), testing rates remain low. The purpose of this proof of concept study was to assess the feasibility of text messaging to promote HCV testing among birth cohort patients. Methods Participants were assigned to receive a text message to promote HCV testing, or a general health message as a control. Participants were sent the message immediately prior to an upcoming appointment. Patients not enrolled in the study were in the standard-of-care group. To assess the impact of the text on HCV test orders on the appointment date participant charts were reviewed. Results The sample was largely non-Hispanic, Caucasian, and female. Of participants sent the HCV message (n = 22), 50.0% had a test ordered, compared to 41.7% and 27.5% in the control (n = 13) and standard-of-care groups (n = 69), respectively. Conclusion This proof of concept study demonstrated the feasibility of text messaging to promote HCV testing among birth cohort patients. Those receiving the HCV message were more likely to have an HCV test ordered compared to those who received no message, although this difference was not statistically significant. A larger study is needed to confirm these results.

Practicing Clinicians' Recommendations to Reduce Burden from the Electronic Health Record Inbox: a Mixed-Methods Study.

BACKGROUND: Workload from electronic health record (EHR) inbox notifications leads to information overload and contributes to job dissatisfaction and physician burnout. Better understanding of physicians' inbox requirements and workflows could optimize inbox designs, enhance efficiency, and reduce safety risks from information overload. DESIGN: We conducted a mixed-methods study to identify strategies to enhance EHR inbox design and workflow. First, we performed a secondary analysis of national survey data of all Department of Veterans Affairs (VA) primary care practitioners (PCP) to identify major themes in responses to a free-text question soliciting suggestions to improve EHR inbox design and workflows. We then conducted expert interviews of clinicians at five health care systems (1 VA and 4 non-VA settings using 4 different EHRs) to understand existing optimal strategies to improve efficiency and situational awareness related to EHR inbox use. Themes from survey data were cross-validated with interview findings. RESULTS: We analyzed responses from 2104 PCPs who completed the free-text inbox question (of 5001 PCPs who responded to survey) and used an inductive approach to identify five themes: (1) Inbox notification content should be actionable for patient care and relevant to recipient clinician, (2) Inboxes should reduce risk of losing messages, (3) Inbox functionality should be optimized to improve efficiency of processing notifications, (4) Team support should be leveraged to help with EHR inbox notification burden, (5) Sufficient time should be provided to all clinicians to process EHR inbox notifications. We subsequently interviewed 15 VA and non-VA clinicians and identified 11 unique strategies, each corresponding directly with one of these five themes. CONCLUSION: Feedback from practicing end-user clinicians provides robust evidence to improve content and design of the EHR inbox and related clinical workflows and organizational policies. Several strategies we identified could improve clinicians' EHR efficiency and satisfaction as well as empower them to work with their local administrators, health IT personnel, and EHR developers to improve these systems.

Barriers and facilitators impacting reliability of the electronic health record-facilitated total testing process.

OBJECTIVE: Despite ongoing efforts to improve reliability of the total testing process (TTP), breakdowns continue to occur resulting in diagnostic delays and suboptimal patient outcomes. We performed an exploratory study to identify factors that impact TTP reliability in electronic health record (EHR)-enabled care. MATERIALS AND METHODS: We interviewed experts at three large EHR-enabled health care organizations and identified all TTP steps performed from clinician test ordering to result communication to patients. Findings from all sites were combined to develop a detailed process map of known TTP activities. We additionally asked experts about factors that positively or negatively impacted TTP reliability at each step. We describe the specific TTP steps identified and associated barriers and facilitators to TTP reliability. RESULTS: We interviewed 39 experts involved in or overseeing the TTP. Most TTP activities identified were similar across sites, but we found significant differences with test order transmission to diagnostic services and relay of results back to clinicians and patients. Twenty-five unique barriers were identified related to technology and EHR usability issues, time and resource constraints, suboptimal clinic workflows, patient-related factors, information access limitations, and insufficient clinician training. Twenty-four unique facilitators were identified related to personnel training, workflow optimization and standardization, helpful EHR features, and improved electronic communication between clinics and diagnostic services. DISCUSSION: Barriers related to EHR usability and with communication between clinicians and diagnostic services increase TTP vulnerability and should be targeted by future efforts to improve process reliability. Several facilitators identified in the study could inform future strategies and solutions to improve TTP reliability.

Application of electronic trigger tools to identify targets for improving diagnostic safety.

Progress in reducing diagnostic errors remains slow partly due to poorly defined methods to identify errors, high-risk situations, and adverse events. Electronic trigger (e-trigger) tools, which mine vast amounts of patient data to identify signals indicative of a likely error or adverse event, offer a promising method to efficiently identify errors. The increasing amounts of longitudinal electronic data and maturing data warehousing techniques and infrastructure offer an unprecedented opportunity to implement new types of e-trigger tools that use algorithms to identify risks and events related to the diagnostic process. We present a knowledge discovery framework, the Safer Dx Trigger Tools Framework, that enables health systems to develop and implement e-trigger tools to identify and measure diagnostic errors using comprehensive electronic health record (EHR) data. Safer Dx e-trigger tools detect potential diagnostic events, allowing health systems to monitor event rates, study contributory factors and identify targets for improving diagnostic safety. In addition to promoting organisational learning, some e-triggers can monitor data prospectively and help identify patients at high-risk for a future adverse event, enabling clinicians, patients or safety personnel to take preventive actions proactively. Successful application of electronic algorithms requires health systems to invest in clinical informaticists, information technology professionals, patient safety professionals and clinicians, all of who work closely together to overcome development and implementation challenges. We outline key future research, including advances in natural language processing and machine learning, needed to improve effectiveness of e-triggers. Integrating diagnostic safety e-triggers in institutional patient safety strategies can accelerate progress in reducing preventable harm from diagnostic errors.