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.

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.

Developing the Safer Dx Checklist of Ten Safety Recommendations for Health Care Organizations to Address Diagnostic Errors.

BACKGROUND: Most health care organizations (HCOs) find diagnostic errors hard to address. The research team developed a checklist (the Safer Dx Checklist) of 10 high-priority safety practices HCOs can use to conduct a proactive risk assessment to address diagnostic error. METHODS: First, the team identified potential practices based on reviews of recent literature, reports by national and international organizations, and interviews with quality/safety leaders. Then a Delphi panel was conducted, followed by an online expert panel, to prioritize 10 practices. The prioritization process considered impact on safety and feasibility of practice implementation within a one- to three-year time frame. Finally, cognitive walkthroughs were conducted for a face-validity check with end users. The team also conducted content analysis in each step to look for themes that influenced prioritization or checklist implementation. RESULTS: A total of 71 practices for prioritization were identified through the Delphi panel of 28 experts; 65% of participants reached consensus on 28 practices. A multidisciplinary panel of 10 experts helped prioritize and refine the top 10 practices, which were then developed into a checklist paired with implementation guidance. Practices included themes related to creating organizational and leadership accountability for improving diagnosis, including patients in diagnostic safety work, and developing and implementing organizational infrastructure for measurement and improvement activities. Qualitative analysis revealed insights for implementation. End users at three different HCOs helped refine implementation guidance for the checklist. CONCLUSION: The researchers identified 10 safety practices to help organizations conduct a proactive, systematic assessment of risks to timely and accurate diagnosis. The Safer Dx Checklist can enable HCOs to begin implementing strategies to address diagnostic error.

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.

Creating a Learning Health System for Improving Diagnostic Safety: Pragmatic Insights from US Health Care Organizations.

OBJECTIVE: To identify challenges and pragmatic strategies for improving diagnostic safety at an organizational level using concepts from learning health systems METHODS: We interviewed 32 safety leaders across the USA on how their organizations approach diagnostic safety. Participants were recruited through email and represented geographically diverse academic and non-academic settings. The interview included questions on culture of reporting and learning from diagnostic errors; data gathering and analysis activities; diagnostic training and educational activities; and engagement of clinical leadership, staff, patients, and families in diagnostic safety activities. We conducted an inductive content analysis of interview transcripts and two reviewers coded all data. RESULTS: Of 32 participants, 12 reported having a specific program to address diagnostic errors. Multiple barriers to implement diagnostic safety activities emerged: serious concerns about psychological safety associated with diagnostic error; lack of infrastructure for measurement, monitoring, and improvement activities related to diagnosis; lack of leadership investment, which was often diverted to competing priorities related to publicly reported measures or other incentives; and lack of dedicated teams to work on diagnostic safety. Participants provided several strategies to overcome barriers including adapting trigger tools to identify safety events, engaging patients in diagnostic safety, and appointing dedicated diagnostic safety champions. CONCLUSIONS: Several foundational building blocks related to learning health systems could inform organizational efforts to reduce diagnostic error. Promoting an organizational culture specific to diagnostic safety, using science and informatics to improve measurement and analysis, leadership incentives to build institutional capacity to address diagnostic errors, and patient engagement in diagnostic safety activities can enable progress.

Inviting patients to identify diagnostic concerns through structured evaluation of their online visit notes.

BACKGROUND: The 21st Century Cures Act mandates patients' access to their electronic health record (EHR) notes. To our knowledge, no previous work has systematically invited patients to proactively report diagnostic concerns while documenting and tracking their diagnostic experiences through EHR-based clinician note review. OBJECTIVE: To test if patients can identify concerns about their diagnosis through structured evaluation of their online visit notes. METHODS: In a large integrated health system, patients aged 18-85 years actively using the patient portal and seen between October 2019 and February 2020 were invited to respond to an online questionnaire if an EHR algorithm detected any recent unexpected return visit following an initial primary care consultation ("at-risk" visit). We developed and tested an instrument (Safer Dx Patient Instrument) to help patients identify concerns related to several dimensions of the diagnostic process based on notes review and recall of recent "at-risk" visits. Additional questions assessed patients' trust in their providers and their general feelings about the visit. The primary outcome was a self-reported diagnostic concern. Multivariate logistic regression tested whether the primary outcome was predicted by instrument variables. RESULTS: Of 293 566 visits, the algorithm identified 1282 eligible patients, of whom 486 responded. After applying exclusion criteria, 418 patients were included in the analysis. Fifty-one patients (12.2%) identified a diagnostic concern. Patients were more likely to report a concern if they disagreed with statements "the care plan the provider developed for me addressed all my medical concerns" [odds ratio (OR), 2.65; 95% confidence interval [CI], 1.45-4.87) and "I trust the provider that I saw during my visit" (OR, 2.10; 95% CI, 1.19-3.71) and agreed with the statement "I did not have a good feeling about my visit" (OR, 1.48; 95% CI, 1.09-2.01). CONCLUSION: Patients can identify diagnostic concerns based on a proactive online structured evaluation of visit notes. This surveillance strategy could potentially improve transparency in the diagnostic process.

Improving diagnostic performance through feedback: the Diagnosis Learning Cycle.

BACKGROUND: Errors in reasoning are a common cause of diagnostic error. However, it is difficult to improve performance partly because providers receive little feedback on diagnostic performance. Examining means of providing consistent feedback and enabling continuous improvement may provide novel insights for diagnostic performance. METHODS: We developed a model for improving diagnostic performance through feedback using a six-step qualitative research process, including a review of existing models from within and outside of medicine, a survey, semistructured interviews with individuals working in and outside of medicine, the development of the new model, an interdisciplinary consensus meeting, and a refinement of the model. RESULTS: We applied theory and knowledge from other fields to help us conceptualise learning and comparison and translate that knowledge into an applied diagnostic context. This helped us develop a model, the Diagnosis Learning Cycle, which illustrates the need for clinicians to be given feedback about both their confidence and reasoning in a diagnosis and to be able to seamlessly compare diagnostic hypotheses and outcomes. This information would be stored in a repository to allow accessibility. Such a process would standardise diagnostic feedback and help providers learn from their practice and improve diagnostic performance. This model adds to existing models in diagnosis by including a detailed picture of diagnostic reasoning and the elements required to improve outcomes and calibration. CONCLUSION: A consistent, standard programme of feedback that includes representations of clinicians' confidence and reasoning is a common element in non-medical fields that could be applied to medicine. Adapting this approach to diagnosis in healthcare is a promising next step. This information must be stored reliably and accessed consistently. The next steps include testing the Diagnosis Learning Cycle in clinical settings.

Use of patient complaints to identify diagnosis-related safety concerns: a mixed-method evaluation.

BACKGROUND: Patient complaints are associated with adverse events and malpractice claims but underused in patient safety improvement. OBJECTIVE: To systematically evaluate the use of patient complaint data to identify safety concerns related to diagnosis as an initial step to using this information to facilitate learning and improvement. METHODS: We reviewed patient complaints submitted to Geisinger, a large healthcare organisation in the USA, from August to December 2017 (cohort 1) and January to June 2018 (cohort 2). We selected complaints more likely to be associated with diagnostic concerns in Geisinger's existing complaint taxonomy. Investigators reviewed all complaint summaries and identified cases as 'concerning' for diagnostic error using the National Academy of Medicine's definition of diagnostic error. For all 'concerning' cases, a clinician-reviewer evaluated the associated investigation report and the patient's medical record to identify any missed opportunities in making a correct or timely diagnosis. In cohort 2, we selected a 10% sample of 'concerning' cases to test this smaller pragmatic sample as a proof of concept for future organisational monitoring. RESULTS: In cohort 1, we reviewed 1865 complaint summaries and identified 177 (9.5%) concerning reports. Review and analysis identified 39 diagnostic errors. Most were categorised as 'Clinical Care issues' (27, 69.2%), defined as concerns/questions related to the care that is provided by clinicians in any setting. In cohort 2, we reviewed 2423 patient complaint summaries and identified 310 (12.8%) concerning reports. The 10% sample (n=31 cases) contained five diagnostic errors. Qualitative analysis of cohort 1 cases identified concerns about return visits for persistent and/or worsening symptoms, interpersonal issues and diagnostic testing. CONCLUSIONS: Analysis of patient complaint data and corresponding medical record review identifies patterns of failures in the diagnostic process reported by patients and families. Health systems could systematically analyse available data on patient complaints to monitor diagnostic safety concerns and identify opportunities for learning and improvement.

A Program to Provide Clinicians with Feedback on Their Diagnostic Performance in a Learning Health System.

PROBLEM: Reducing diagnostic errors requires improving both systems and individual clinical reasoning. One strategy to achieve diagnostic excellence is learning from feedback. However, clinicians remain uncomfortable receiving feedback on their diagnostic performance. Thus, a team of researchers and clinical leaders aimed to develop and implement a diagnostic performance feedback program for learning that mitigates potential clinician discomfort. APPROACH: The program was developed as part of a larger project to create a learning health system around diagnostic safety at Geisinger, a large, integrated health care system in rural Pennsylvania. Steps included identifying potential missed opportunities in diagnosis (MODs) from various sources (for example, risk management, clinician reports, patient complaints); confirming MODs through chart review; and having trained facilitators provide feedback to clinicians about MODs as learning opportunities. The team developed a guide for facilitators to conduct effective diagnostic feedback sessions and surveyed facilitators and recipients about their experiences and perceptions of the feedback sessions. OUTCOMES: 28 feedback sessions occurred from January 2019 to June 2020, involving MODs from emergency medicine, primary care, and hospital medicine. Most facilitators (90.6% [29/32]) reported that recipients were receptive to learning and discussing MODs. Most recipients reported that conversations were constructive and nonpunitive (83.3% [25/30]) and allowed them to take concrete steps toward improving diagnosis (76.7% [23/30]). Both groups believed discussions would improve future diagnostic safety (93.8% [30/32] and 70.0% [21/30], respectively). KEY INSIGHTS AND NEXT STEPS: An institutional program was developed and implemented to deliver diagnostic performance feedback. Such a program may facilitate learning and improvement to reduce MODs. Future efforts should assess long-term effects on diagnostic performance and patient outcomes.

Identifying trigger concepts to screen emergency department visits for diagnostic errors.

OBJECTIVES: The diagnostic process is a vital component of safe and effective emergency department (ED) care. There are no standardized methods for identifying or reliably monitoring diagnostic errors in the ED, impeding efforts to enhance diagnostic safety. We sought to identify trigger concepts to screen ED records for diagnostic errors and describe how they can be used as a measurement strategy to identify and reduce preventable diagnostic harm. METHODS: We conducted a literature review and surveyed ED directors to compile a list of potential electronic health record (EHR) trigger (e-triggers) and non-EHR based concepts. We convened a multidisciplinary expert panel to build consensus on trigger concepts to identify and reduce preventable diagnostic harm in the ED. RESULTS: Six e-trigger and five non-EHR based concepts were selected by the expert panel. E-trigger concepts included: unscheduled ED return to ED resulting in hospital admission, death following ED visit, care escalation, high-risk conditions based on symptom-disease dyads, return visits with new diagnostic/therapeutic interventions, and change of treating service after admission. Non-EHR based signals included: cases from mortality/morbidity conferences, risk management/safety office referrals, ED medical director case referrals, patient complaints, and radiology/laboratory misreads and callbacks. The panel suggested further refinements to aid future research in defining diagnostic error epidemiology in ED settings. CONCLUSIONS: We identified a set of e-trigger concepts and non-EHR based signals that could be developed further to screen ED visits for diagnostic safety events. With additional evaluation, trigger-based methods can be used as tools to monitor and improve ED diagnostic performance.

Developing Health Care Organizations That Pursue Learning and Exploration of Diagnostic Excellence: An Action Plan.

Reducing errors in diagnosis is the next big challenge for patient safety. Diagnostic safety improvement efforts should become a priority for health care organizations, payers, and accrediting bodies; however, external incentives, policies, and practical guidance to develop these efforts are largely absent. In this Perspective, the authors highlight ways in which health care organizations can pursue learning and exploration of diagnostic excellence (LEDE). Building on current evidence and their recent experiences in developing such a learning organization at Geisinger, the authors propose a 5-point action plan and corresponding policy levers to support development of LEDE organizations. These recommendations, which are applicable to many health care organizations, include (1) implementing a virtual hub to coordinate organizational activities for improving diagnosis, such as identifying risks and prioritizing interventions that cross intra-institutional silos while promoting a culture of learning and safety; (2) participating in novel scientific initiatives to generate and translate evidence, given the rapidly evolving "basic science" of diagnostic excellence; (3) avoiding the "tyranny of metrics" by focusing on measurement for improvement rather than using measures to reward or punish; (4) engaging clinicians in activities for improving diagnosis and framing missed opportunities positively as learning opportunities rather than negatively as errors; and (5) developing an accountable culture of engaging and learning from patients, who are often underexplored sources of information. The authors also outline specific policy actions to support organizations in implementing these recommendations. They suggest this action plan can stimulate scientific, practice, and policy progress needed for achieving diagnostic excellence and reducing preventable patient harm.

Ebola US Patient Zero: lessons on misdiagnosis and effective use of electronic health records.

On September 30th, 2014, the Centers for Disease Control and Prevention (CDC) confirmed the first travel-associated case of US Ebola in Dallas, TX. This case exposed two of the greatest concerns in patient safety in the US outpatient health care system: misdiagnosis and ineffective use of electronic health records (EHRs). The case received widespread media attention highlighting failures in disaster management, infectious disease control, national security, and emergency department (ED) care. In addition, an error in making a correct and timely Ebola diagnosis on initial ED presentation brought diagnostic decision-making vulnerabilities in the EHR era into the public eye. In this paper, we use this defining "teachable moment" to highlight the public health challenge of diagnostic errors and discuss the effective use of EHRs in the diagnostic process. We analyze the case to discuss several missed opportunities and outline key challenges and opportunities facing diagnostic decision-making in EHR-enabled health care. It is important to recognize the reality that EHRs suffer from major usability and inter-operability issues, but also to acknowledge that they are only tools and not a replacement for basic history-taking, examination skills, and critical thinking. While physicians and health care organizations ultimately need to own the responsibility for addressing diagnostic errors, several national-level initiatives can help, including working with software developers to improve EHR usability. Multifaceted approaches that account for both technical and non-technical factors will be needed. Ebola US Patient Zero reminds us that in certain cases, a single misdiagnosis can have widespread and costly implications for public health.