Electronic health record-based surveillance of diagnostic errors in primary care

"Closing the loop": a mixed-methods study about resident learning from outcome feedback after patient handoffs

Background Learning patient outcomes is recognized as crucial for ongoing refinement of clinical decision-making, but is often difficult in fragmented care with frequent handoffs. Data on resident habits of seeking outcome feedback after handoffs are lacking. Methods We performed a mixed-methods study including (1) an analysis of chart re-access rates after handoffs performed using access logs of the electronic health record (EHR); and (2) a web-based survey sent to internal medicine (IM) and emergency medicine (EM) residents about their habits of and barriers to learning the outcomes of patients after they have handed them off to other teams. Results Residents on ward rotations were often able to re-access charts of patients after handoffs, but those on EM or night admitting rotations did so <5% of the time. Among residents surveyed, only a minority stated that they frequently find out the outcomes of patients they have handed off, although learning outcomes was important to both their education and job satisfaction. Most were not satisfied with current systems of learning outcomes of patients after handoffs, citing too little time and lack of reliable patient tracking systems as the main barriers. Conclusions Despite perceived importance of learning outcomes after handoffs, residents cite difficulty with obtaining such information. Systematically providing feedback on patient outcomes would meet a recognized need among physicians in training.

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.

Diagnostic Decision-Making in the Emergency Department

Emergency medicine requires diagnosing unfamiliar patients with undifferentiated acute presentations. This requires hypothesis generation and questioning, examination, and testing. Balancing patient load, care across the severity spectrum, and frequent interruptions create time pressures that predispose humans to fast thinking or cognitive shortcuts, including cognitive biases. Diagnostic error is the failure to establish an accurate and timely explanation of the problem or communicate that to the patient, often contributing to physical, emotional, or financial harm. Methods for monitoring diagnostic error in the emergency department are needed to establish frequency and serve as a foundation for future interventions.

The impact of electronic health records on diagnosis

Diagnostic error may be the largest unaddressed patient safety concern in the United States, responsible for an estimated 40,000-80,000 deaths annually. With the electronic health record (EHR) now in near universal use, the goal of this narrative review is to synthesize evidence and opinion regarding the impact of the EHR and health care information technology (health IT) on the diagnostic process and its outcomes. We consider the many ways in which the EHR and health IT facilitate diagnosis and improve the diagnostic process, and conversely the major ways in which it is problematic, including the unintended consequences that contribute to diagnostic error and sometimes patient deaths. We conclude with a summary of suggestions for improving the safety and safe use of these resources for diagnosis in the future.

Diagnostic Discordance, Health Information Exchange, and Inter-Hospital Transfer Outcomes: a Population Study

BACKGROUND

Studying diagnostic error at the population level requires an understanding of how diagnoses change over time.

OBJECTIVE

To use inter-hospital transfers to examine the frequency and impact of changes in diagnosis on patient risk, and whether health information exchange can improve patient safety by enhancing diagnostic accuracy.

DESIGN

Diagnosis coding before and after hospital transfer was merged with responses from the American Hospital Association Annual Survey for a cohort of patients transferred between hospitals to identify predictors of mortality.

PARTICIPANTS

Patients (180,337) 18 years or older transferred between 473 acute care hospitals from NY, FL, IA, UT, and VT from 2011 to 2013.

MAIN MEASURES

We identified discordant Elixhauser comorbidities before and after transfer to determine the frequency and developed a weighted score of diagnostic discordance to predict mortality. This was included in a multivariate model with inpatient mortality as the dependent variable. We investigated whether health information exchange (HIE) functionality adoption as reported by hospitals improved diagnostic discordance and inpatient mortality.

KEY RESULTS

Discordance in diagnoses occurred in 85.5% of all patients. Seventy-three percent of patients gained a new diagnosis following transfer while 47% of patients lost a diagnosis. Diagnostic discordance was associated with increased adjusted inpatient mortality (OR 1.11 95% CI 1.10-1.11, p < 0.001) and allowed for improved mortality prediction. Bilateral hospital HIE participation was associated with reduced diagnostic discordance index (3.69 vs. 1.87%, p < 0.001) and decreased inpatient mortality (OR 0.88, 95% CI 0.89-0.99, p < 0.001).

CONCLUSIONS

Diagnostic discordance commonly occurred during inter-hospital transfers and was associated with increased inpatient mortality. Health information exchange adoption was associated with decreased discordance and improved patient outcomes.

Electronic health record reviews to measure diagnostic uncertainty in primary care

RATIONALE, AIMS AND OBJECTIVES

Diagnostic uncertainty is common in primary care. Because it is challenging to measure, there is inadequate scientific understanding of diagnostic decision-making during uncertainty. Our objective was to understand how diagnostic uncertainty was documented in the electronic health record (EHR) and explore a strategy to retrospectively identify it using clinician documentation.

METHODS

We reviewed the literature to identify documentation language that could identify both direct expression and indirect inference of diagnostic uncertainty and designed an instrument to facilitate record review. Direct expression included clinician's use of question marks, differential diagnoses, symptoms as diagnosis, or vocabulary such as "probably, maybe, likely, unclear or unknown," while describing the diagnosis. Indirect inference included absence of documented diagnosis at the end of the visit, ordering of multiple consultations or diagnostic tests to resolve diagnostic uncertainty, and use of suspended judgement, test of treatment, and risk-averse disposition. Two physician-reviewers independently reviewed notes on a sample of outpatient visits to identify diagnostic uncertainty at the end of the visit. Documented Ninth Revision of the International Classification of Diseases (ICD-9) diagnosis codes and note quality were assessed.

RESULTS

Of 389 patient records reviewed, 218 had evidence of diagnostic activity and were included. In 156 visits (71.6%), reviewers identified clinicians who experienced diagnostic uncertainty with moderate inter-reviewer agreement (81.7%; Cohen's kappa: 0.609). Most cases (125, 80.1%) showed evidence of both direct expression and indirect inference. Uncertainty was directly expressed in 139 (89.1%) cases, most commonly by using symptoms as diagnosis (98, 62.8%), and inferred in 144 (92.3%). In more than 1/3 of visits (58, 37.2%), diagnostic uncertainty was recorded inappropriately using ICD-9 codes.

CONCLUSIONS

While current diagnosis coding mechanisms (ICD-9 and ICD-10) are unable to capture uncertainty, our study finds that review of EHR documentation can help identify diagnostic uncertainty with moderate reliability. Better measurement and understanding of diagnostic uncertainty could help inform strategies to improve the safety and efficiency of diagnosis.

What's to Be Done About Laboratory Quality? Process Indicators, Laboratory Stewardship, the Outcomes Problem, Risk Assessment, and Economic Value: Responding to Contemporary Global Challenges

OBJECTIVES

For 50 years, structure, process, and outcomes measures have assessed health care quality. For clinical laboratories, structural quality has generally been assessed by inspection. For assessing process, quality indicators (QIs), statistical monitors of steps in the clinical laboratory total testing, have proliferated across the globe. Connections between structural and process laboratory measures and patient outcomes, however, have rarely been demonstrated.

METHODS

To inform further development of clinical laboratory quality systems, we conducted a selective but worldwide review of publications on clinical laboratory quality assessment.

RESULTS

Some QIs, like seven generic College of American Pathologists Q-Tracks monitors, have demonstrated significant process improvement; other measures have uncovered critical opportunities to improve test selection and result management. The College of Pathologists of Australasia Key Indicator Monitoring and Management System has deployed risk calculations, introduced from failure mode effects analysis, as surrogate measures for outcomes. Showing economic value from clinical laboratory testing quality is a challenge.

CONCLUSIONS

Clinical laboratories should converge on fewer (7-14) rather than more (21-35) process monitors; monitors should cover all steps of the testing process under laboratory control and include especially high-risk specimen-quality QIs. Clinical laboratory stewardship, the combination of education interventions among clinician test orderers and report consumers with revision of test order formats and result reporting schemes, improves test ordering, but improving result reception is more difficult. Risk calculation reorders the importance of quality monitors by balancing three probabilities: defect frequency, weight of potential harm, and detection difficulty. The triple approach of (1) a more focused suite of generic consensus quality indicators, (2) more active clinical laboratory testing stewardship, and (3) integration of formal risk assessment, rather than competing with economic value, enhances it.

Symptom-Disease Pair Analysis of Diagnostic Error (SPADE): a conceptual framework and methodological approach for unearthing misdiagnosis-related harms using big data

BACKGROUND

The public health burden associated with diagnostic errors is likely enormous, with some estimates suggesting millions of individuals are harmed each year in the USA, and presumably many more worldwide. According to the US National Academy of Medicine, improving diagnosis in healthcare is now considered 'a moral, professional, and public health imperative.' Unfortunately, well-established, valid and readily available operational measures of diagnostic performance and misdiagnosis-related harms are lacking, hampering progress. Existing methods often rely on judging errors through labour-intensive human reviews of medical records that are constrained by poor clinical documentation, low reliability and hindsight bias.

METHODS

Key gaps in operational measurement might be filled via thoughtful statistical analysis of existing large clinical, billing, administrative claims or similar data sets. In this manuscript, we describe a method to quantify and monitor diagnostic errors using an approach we call 'Symptom-Disease Pair Analysis of Diagnostic Error' (SPADE).

RESULTS

We first offer a conceptual framework for establishing valid symptom-disease pairs illustrated using the well-known diagnostic error dyad of dizziness-stroke. We then describe analytical methods for both look-back (case-control) and look-forward (cohort) measures of diagnostic error and misdiagnosis-related harms using 'big data'. After discussing the strengths and limitations of the SPADE approach by comparing it to other strategies for detecting diagnostic errors, we identify the sources of validity and reliability that undergird our approach.

CONCLUSION

SPADE-derived metrics could eventually be used for operational diagnostic performance dashboards and national benchmarking. This approach has the potential to transform diagnostic quality and safety across a broad range of clinical problems and settings.

Implementation of diagnostic pauses in the ambulatory setting

BACKGROUND

Diagnostic errors result in preventable morbidity and mortality. The outpatient setting may be at increased risk, where time constraints, the indolent nature of outpatient complaints and single decision-maker practice models predominate.

METHODS

We developed a self-administered diagnostic pause to address diagnostic error. Clinicians (physicians and nurse practitioners) in an academic primary care setting received the tool if they were seeing urgent care patients who had previously been seen in the past two weeks in urgent care. We used pre-post-intervention surveys, focus groups and chart audits 6 months after the urgent care visit to assess the impact of the intervention on participant perceptions and actions.

RESULTS

We piloted diagnostic pauses in two phases (3 months and 6 months, respectively); 9 physicians participated in the first phase, and 16 physicians and 2 nurse practitioners in the second phase. Subjects received 135 alerts for diagnostic pauses and responded to 82 (61% response). Thirteen per cent of alerts resulted in clinicians reporting new actions as a result of the diagnostic pauses. Thirteen per cent of cases at a 6-month chart audit resulted in diagnostic discrepancies, defined as differences in diagnosis from the initial working diagnosis. Focus groups reported that the diagnostic pauses were brief and fairly well integrated into the overall workflow for evaluation but would have benefited as a real-time application for patients at higher risk for diagnostic error.

CONCLUSION

This pilot represents the first known examination of diagnostic pauses in the outpatient setting, and this work potentially paves the way for more broad-based systems and/or electronic interventions to address diagnostic error.

Diagnostic Errors: Impact of an Educational Intervention on Pediatric Primary Care

INTRODUCTION

The purpose of our study was to determine the impact of an educational program on a provider's knowledge related to diagnostic errors and diagnostic reasoning strategies.

METHODS

A quasi-experimental interventional study with a multimedia approach, case study discussion, and trigger-generated medical record review at two time points was conducted. Measurement tools included a test developed by the National Patient Safety Foundation, Reducing Diagnostic Errors: Strategies for Solutions Quiz, additional diagnostic reasoning questions, and a trigger-generated process to analyze medical records.

RESULTS

Knowledge related to diagnostic errors statistically improved from the pretest to posttest scores with sustained 60-day differences (p < .025). Although there was a decline in the proportion of patients returning with the same chief complaint within 14 days, this was not statistically significant (p < .15). When providers were confronted with an unrecognizable clinical presentation, they reported an increased use of a "diagnostic timeout" (p < .038).

DISCUSSION

Providers developed an increased awareness of the presence of diagnostic errors in the primary care setting, the contributing risk factors for a diagnostic error, and possible strategies to reduce diagnostic errors. These factors had an unexpected impact on changing the primary care practice model to enhance the continuity of patient care.

Development and Validation of Trigger Algorithms to Identify Delays in Diagnostic Evaluation of Gastroenterological Cancer

BACKGROUND & AIMS

Colorectal cancer (CRC) and hepatocellular cancer (HCC) are common causes of death and morbidity, and patients benefit from early detection. However, delays in follow-up of suspicious findings are common, and methods to efficiently detect such delays are needed. We developed, refined, and tested trigger algorithms that identify patients with delayed follow-up evaluation of findings suspicious of CRC or HCC.

METHODS

We developed and validated two trigger algorithms that detect delays in diagnostic evaluation of CRC and HCC using laboratory, diagnosis, procedure, and referral codes from the Department of Veteran Affairs National Corporate Data Warehouse. The algorithm initially identified patients with positive test results for iron deficiency anemia or fecal immunochemical test (for CRC) and elevated α-fetoprotein results (for HCC). Our algorithm then excluded patients for whom follow-up evaluation was unnecessary, such as patients with a terminal illness or those who had already completed a follow-up evaluation within 60 days. Clinicians reviewed samples of both delayed and nondelayed records, and review data were used to calculate trigger performance.

RESULTS

We applied the algorithm for CRC to 245,158 patients seen from January 1, 2013, through December 31, 2013 and identified 1073 patients with delayed follow up. In a review of 400 randomly selected records, we found that our algorithm identified patients with delayed follow-up with a positive predictive value of 56.0% (95% CI, 51.0%-61.0%). We applied the algorithm for HCC to 333,828 patients seen from January 1, 2011 through December 31, 2014, and identified 130 patients with delayed follow-up. During manual review of all 130 records, we found that our algorithm identified patients with delayed follow-up with a positive predictive value of 82.3% (95% CI, 74.4%-88.2%). When we extrapolated the findings to all patients with abnormal results, the algorithm identified patients with delayed follow-up evaluation for CRC with 68.6% sensitivity (95% CI, 65.4%-71.6%) and 81.1% specificity (95% CI, 79.5%-82.6%); it identified patients with delayed follow-up evaluation for HCC with 89.1% sensitivity (95% CI, 81.8%-93.8%) and 96.5% specificity (95% CI, 94.8%-97.7%). Compared to nonselective methods, use of the algorithm reduced the number of records required for review to identify a delay by more than 99%.

CONCLUSIONS

Using data from the Veterans Affairs electronic health record database, we developed an algorithm that greatly reduces the number of record reviews necessary to identify delays in follow-up evaluations for patients with suspected CRC or HCC. This approach offers a more efficient method to identify delayed diagnostic evaluation of gastroenterological cancers.

Using Fault Trees to Advance Understanding of Diagnostic Errors

PROBLEM DEFINITION

Diagnostic errors annually affect at least 5% of adults in the outpatient setting in the United States. Formal analytic techniques are only infrequently used to understand them, in part because of the complexity of diagnostic processes and clinical work flows involved. In this article, diagnostic errors were modeled using fault tree analysis (FTA), a form of root cause analysis that has been successfully used in other high-complexity, high-risk contexts. How factors contributing to diagnostic errors can be systematically modeled by FTA to inform error understanding and error prevention is demonstrated.

INITIAL APPROACH

A team of three experts reviewed 10 published cases of diagnostic error and constructed fault trees. The fault trees were modeled according to currently available conceptual frameworks characterizing diagnostic error. The 10 trees were then synthesized into a single fault tree to identify common contributing factors and pathways leading to diagnostic error.

KEY INSIGHTS

FTA is a visual, structured, deductive approach that depicts the temporal sequence of events and their interactions in a formal logical hierarchy. The visual FTA enables easier understanding of causative processes and cognitive and system factors, as well as rapid identification of common pathways and interactions in a unified fashion. In addition, it enables calculation of empirical estimates for causative pathways. Thus, fault trees might provide a useful framework for both quantitative and qualitative analysis of diagnostic errors.

NEXT STEPS

Future directions include establishing validity and reliability by modeling a wider range of error cases, conducting quantitative evaluations, and undertaking deeper exploration of other FTA capabilities.

Errors in Diagnosis of Spinal Epidural Abscesses in the Era of Electronic Health Records

PURPOSE

With this study, we set out to identify missed opportunities in diagnosis of spinal epidural abscesses to outline areas for process improvement.

METHODS

Using a large national clinical data repository, we identified all patients with a new diagnosis of spinal epidural abscess in the Department of Veterans Affairs (VA) during 2013. Two physicians independently conducted retrospective chart reviews on 250 randomly selected patients and evaluated their records for red flags (eg, unexplained weight loss, neurological deficits, and fever) 90 days prior to diagnosis. Diagnostic errors were defined as missed opportunities to evaluate red flags in a timely or appropriate manner. Reviewers gathered information about process breakdowns related to patient factors, the patient-provider encounter, test performance and interpretation, test follow-up and tracking, and the referral process. Reviewers also determined harm and time lag between red flags and definitive diagnoses.

RESULTS

Of 250 patients, 119 had a new diagnosis of spinal epidural abscess, 66 (55.5%) of which experienced diagnostic error. Median time to diagnosis in error cases was 12 days, compared with 4 days in cases without error (P <.01). Red flags that were frequently not evaluated in error cases included unexplained fever (n = 57; 86.4%), focal neurological deficits with progressive or disabling symptoms (n = 54; 81.8%), and active infection (n = 54; 81.8%). Most errors involved breakdowns during the patient-provider encounter (n = 60; 90.1%), including failures in information gathering/integration, and were associated with temporary harm (n = 43; 65.2%).

CONCLUSION

Despite wide availability of clinical data, errors in diagnosis of spinal epidural abscesses are common and involve inadequate history, physical examination, and test ordering. Solutions should include renewed attention to basic clinical skills.

The global burden of diagnostic errors in primary care

Diagnosis is one of the most important tasks performed by primary care physicians. The World Health Organization (WHO) recently prioritized patient safety areas in primary care, and included diagnostic errors as a high-priority problem. In addition, a recent report from the Institute of Medicine in the USA, 'Improving Diagnosis in Health Care', concluded that most people will likely experience a diagnostic error in their lifetime. In this narrative review, we discuss the global significance, burden and contributory factors related to diagnostic errors in primary care. We synthesize available literature to discuss the types of presenting symptoms and conditions most commonly affected. We then summarize interventions based on available data and suggest next steps to reduce the global burden of diagnostic errors. Research suggests that we are unlikely to find a 'magic bullet' and confirms the need for a multifaceted approach to understand and address the many systems and cognitive issues involved in diagnostic error. Because errors involve many common conditions and are prevalent across all countries, the WHO's leadership at a global level will be instrumental to address the problem. Based on our review, we recommend that the WHO consider bringing together primary care leaders, practicing frontline clinicians, safety experts, policymakers, the health IT community, medical education and accreditation organizations, researchers from multiple disciplines, patient advocates, and funding bodies among others, to address the many common challenges and opportunities to reduce diagnostic error. This could lead to prioritization of practice changes needed to improve primary care as well as setting research priorities for intervention development to reduce diagnostic error.

Teaching for Reducing Diagnostic Errors

Diagnostic errors, constituted by a missed, wrong, or delayed diagnosis detected later by additional tests or findings, are one of the most vexing issues in medicine. They are one of the commonest causes of patient- harm and also medical negligence claims. Although a variety of constructs have been proposed to explain diagnostic errors, the complex interplay of cognitive- and system-factors that underlie these errors is rarely clear to the clinicians. In this write-up, we discuss the reasons for diagnostic errors and how medical students can be trained to avoid such errors. The errors have been classified as Cognitive errors, System errors, and No-fault errors, and cognitive interventions to address each of these are detailed.

Finding Diagnostic Errors in Children Admitted to the PICU

OBJECTIVES

To determine whether the Safer Dx Instrument, a structured tool for finding diagnostic errors in primary care, can be used to reliably detect diagnostic errors in patients admitted to a PICU.

DESIGN AND SETTING

The Safer Dx Instrument consists of 11 questions to evaluate the diagnostic process and a final question to determine if diagnostic error occurred. We used the instrument to analyze four "high-risk" patient cohorts admitted to the PICU between June 2013 and December 2013.

PATIENTS

High-risk cohorts were defined as cohort 1: patients who were autopsied; cohort 2: patients seen as outpatients within 2 weeks prior to PICU admission; cohort 3: patients transferred to PICU unexpectedly from an acute care floor after a rapid response and requiring vasoactive medications and/or endotracheal intubation due to decompensation within 24 hours; and cohort 4: patients transferred to PICU unexpectedly from an acute care floor after a rapid response without subsequent decompensation in 24 hours.

INTERVENTIONS

Two clinicians used the instrument to independently review records in each cohort for diagnostic errors, defined as missed opportunities to make a correct or timely diagnosis. Errors were confirmed by senior expert clinicians.

MEASUREMENTS AND MAIN RESULTS

Diagnostic errors were present in 26 of 214 high-risk patient records (12.1%; 95% CI, 8.2-17.5%) with the following frequency distribution: cohort 1: two of 16 (12.5%); cohort 2: one of 41 (2.4%); cohort 3: 13 of 44 (29.5%); and cohort 4: 10 of 113 (8.8%). Overall initial reviewer agreement was 93.6% (κ, 0.72). Infections and neurologic conditions were the most commonly missed diagnoses across all high-risk cohorts (16/26).

CONCLUSIONS

The Safer Dx Instrument has high reliability and validity for diagnostic error detection when used in high-risk pediatric care settings. With further validation in additional clinical settings, it could be useful to enhance learning and feedback about diagnostic safety in children.

A patient feedback reporting tool for OpenNotes: implications for patient-clinician safety and quality partnerships

BACKGROUND

OpenNotes, a national movement inviting patients to read their clinicians' notes online, may enhance safety through patient-reported documentation errors.

OBJECTIVE

To test an OpenNotes patient reporting tool focused on safety concerns.

METHODS

We invited 6225 patients through a patient portal to provide note feedback in a quality improvement pilot between August 2014 and 2015. A link at the end of the note led to a 9-question survey. Patient Relations personnel vetted responses, shared safety concerns with providers and documented whether changes were made.

RESULTS

2736/6225(44%) of patients read notes; among these, 1 in 12 patients used the tool, submitting 260 reports. Nearly all (96%) respondents reported understanding the note. Patients and care partners documented potential safety concerns in 23% of reports; 2% did not understand the care plan and 21% reported possible mistakes, including medications, existing health problems, something important missing from the note or current symptoms. Among these, 64% were definite or possible safety concerns on clinician review, and 57% of cases confirmed with patients resulted in a change to the record or care. The feedback tool exceeded the reporting rate of our ambulatory online clinician adverse event reporting system several-fold. After a year, 99% of patients and care partners found the tool valuable, 97% wanted it to continue, 98% reported unchanged or improved relationships with their clinician, and none of the providers in the small pilot reported worsening workflow or relationships with patients.

CONCLUSIONS

Patients and care partners reported potential safety concerns in about one-quarter of reports, often resulting in a change to the record or care. Early data from an OpenNotes patient reporting tool may help engage patients as safety partners without apparent negative consequences for clinician workflow or patient-clinician relationships.

Challenges in patient safety improvement research in the era of electronic health records

Electronic health record (EHR) data repositories contain large volumes of aggregated, longitudinal clinical data that could allow patient safety researchers to identify important safety issues and conduct comprehensive evaluations of health care delivery outcomes. However, few health systems have successfully converted this abundance of data into useful information or knowledge for safety improvement. In this paper, we use a case study involving a project on missed/delayed follow-up of test results to discuss real-world challenges in using EHR data for patient safety research. We identify three types of challenges that pose as barriers to advance patient safety improvement research: 1) gaining approval to access/review EHR data; 2) interpreting EHR data; 3) working with local IT/EHR personnel. We discuss the complexity of these challenges, all of which are unlikely to be unique to this project, and outline some key next steps that must be taken to support research that uses EHR data to improve safety. We recognize that all organizations face competing priorities between clinical operations and research. However, to leverage EHRs and their abundant data for patient safety improvement research, many current data access and security policies and procedures must be rewritten and standardized across health care organizations. These efforts are essential to help make EHRs and EHR data useful for progress in our journey to safer health care.

Accuracy of the Safer Dx Instrument to Identify Diagnostic Errors in Primary Care

IMPORTANCE

Diagnostic errors are common and harmful, but difficult to define and measure. Measurement of diagnostic errors often depends on retrospective medical record reviews, frequently resulting in reviewer disagreement.

OBJECTIVES

We aimed to test the accuracy of an instrument to help detect presence or absence of diagnostic error through record reviews.

DESIGN

We gathered questions from several previously used instruments for diagnostic error measurement, then developed and refined our instrument. We tested the accuracy of the instrument against a sample of patient records (n = 389), with and without previously identified diagnostic errors (n = 129 and n = 260, respectively).

RESULTS

The final version of our instrument (titled Safer Dx Instrument) consisted of 11 questions assessing diagnostic processes in the patient-provider encounter and a main outcome question to determine diagnostic error. In comparison with the previous sample, the instrument yielded an overall accuracy of 84 %, sensitivity of 71 %, specificity of 90 %, negative predictive value of 86 %, and positive predictive value of 78 %. All 11 items correlated significantly with the instrument's error outcome question (all p values ≤ 0.01). Using factor analysis, the 11 questions clustered into two domains with high internal consistency (initial diagnostic assessment, and performance and interpretation of diagnostic tests) and a patient factor domain with low internal consistency (Cronbach's alpha coefficients 0.93, 0.92, and 0.38, respectively).

CONCLUSIONS

The Safer Dx Instrument helps quantify the likelihood of diagnostic error in primary care visits, achieving a high degree of accuracy for measuring their presence or absence. This instrument could be useful to identify high-risk cases for further study and quality improvement.

Diagnostic errors related to acute abdominal pain in the emergency department

OBJECTIVE

Diagnostic errors in the emergency department (ED) are harmful and costly. We reviewed a selected high-risk cohort of patients presenting to the ED with abdominal pain to evaluate for possible diagnostic errors and associated process breakdowns.

DESIGN

We conducted a retrospective chart review of ED patients >18 years at an urban academic hospital. A computerised 'trigger' algorithm identified patients possibly at high risk for diagnostic errors to facilitate selective record reviews. The trigger determined patients to be at high risk because they: (1) presented to the ED with abdominal pain, and were discharged home and (2) had a return ED visit within 10 days that led to a hospitalisation. Diagnostic errors were defined as missed opportunities to make a correct or timely diagnosis based on the evidence available during the first ED visit, regardless of patient harm, and included errors that involved both ED and non-ED providers. Errors were determined by two independent record reviewers followed by team consensus in cases of disagreement.

RESULTS

Diagnostic errors occurred in 35 of 100 high-risk cases. Over two-thirds had breakdowns involving the patient-provider encounter (most commonly history-taking or ordering additional tests) and/or follow-up and tracking of diagnostic information (most commonly follow-up of abnormal test results). The most frequently missed diagnoses were gallbladder pathology (n=10) and urinary infections (n=5).

CONCLUSIONS

Diagnostic process breakdowns in ED patients with abdominal pain most commonly involved history-taking, ordering insufficient tests in the patient-provider encounter and problems with follow-up of abnormal test results.

Electronic Trigger-Based Intervention to Reduce Delays in Diagnostic Evaluation for Cancer: A Cluster Randomized Controlled Trial

PURPOSE

We tested whether prospective use of electronic health record-based trigger algorithms to identify patients at risk of diagnostic delays could prevent delays in diagnostic evaluation for cancer.

METHODS

We performed a cluster randomized controlled trial of primary care providers (PCPs) at two sites to test whether triggers that prospectively identify patients with potential delays in diagnostic evaluation for lung, colorectal, or prostate cancer can reduce time to follow-up diagnostic evaluation. Intervention steps included queries of the electronic health record repository for patients with abnormal findings and lack of associated follow-up actions, manual review of triggered records, and communication of this information to PCPs via secure e-mail and, if needed, phone calls to ensure message receipt. We compared times to diagnostic evaluation and proportions of patients followed up between intervention and control cohorts based on final review at 7 months.

RESULTS

We recruited 72 PCPs (36 in the intervention group and 36 in the control group) and applied the trigger to all patients under their care from April 20, 2011, to July 19, 2012. Of 10,673 patients with abnormal findings, the trigger flagged 1,256 patients (11.8%) as high risk for delayed diagnostic evaluation. Times to diagnostic evaluation were significantly lower in intervention patients compared with control patients flagged by the colorectal trigger (median, 104 v 200 days, respectively; n = 557; P < .001) and prostate trigger (40% received evaluation at 144 v 192 days, respectively; n = 157; P < .001) but not the lung trigger (median, 65 v 93 days, respectively; n = 19; P = .59). More intervention patients than control patients received diagnostic evaluation by final review (73.4% v 52.2%, respectively; relative risk, 1.41; 95% CI, 1.25 to 1.58).

CONCLUSION

Electronic trigger-based interventions seem to be effective in reducing time to diagnostic evaluation of colorectal and prostate cancer as well as improving the proportion of patients who receive follow-up. Similar interventions could improve timeliness of diagnosis of other serious conditions.

Use of an Expedited Review Tool to Screen for Prior Diagnostic Error in Emergency Department Patients

OBJECTIVE

Missed diagnoses are an important area of care quality resulting in significant morbidity and mortality. Determination of rates and causes has been limited by difficulties in screening, including the effort of manual chart review. We developed and tested a semi- automated review tool to expedite screening for diagnostic errors in an electronic health record (EHR).

METHODS

We retrospectively reviewed patients seen in the emergency department (ED) of a teaching hospital over 31 days, using an automated screen to identify those with a prior in-system visit during the 14 days preceding their ED visit. We collected prior and subsequent notes from the institution's EHR for these cases, then populated a specially designed relational database enabling rapid comparison of prior visit records to the sentinel ED visit. Each case was assessed for potential missed or delayed diagnosis, and rated by likelihood as "definite, probable, possible, unlikely or none."

RESULTS

A total of 5 066 patient encounters were screened by a clinician using the tool, of which 1 498 (30%) had a clinical encounter within the preceding 14 days. Of these, 37 encounters (2.6% of those reviewed) were "definite" or "probable" missed diagnoses. The rapid review tool took a mean of 1.9 minutes per case for primary review, compared with 11.2 minutes per case for reviews without the automated tool.

CONCLUSIONS

Diagnostic errors were present in a significant number of cases presenting to the ED after recent healthcare visits. An innovative review tool enabled a substantially increased efficiency in screening for diagnostic errors.

Missed diagnostic opportunities and English general practice: a study to determine their incidence, confounding and contributing factors and potential impact on patients through retrospective review of electronic medical records

Background

Patient safety research has focused largely on hospital settings despite the fact that in many countries, the majority of patient contacts are in primary care. The knowledge base about patient safety in primary care is developing but sparse and diagnostic error is a relatively understudied and an unmeasured area of patient safety. Diagnostic error rates vary according to how ‘error’ is defined but one suggested hallmark is clear evidence of ‘missed opportunity’ (MDOs) makes a correct or timely diagnosis to prevent them. While there is no agreed definition or method of measuring MDOs, retrospective manual chart or patient record reviews are a ‘gold standard’. This study protocol aims to (1) determine the incidence of MDOs in English general practice, (2) identify the confounding and contributing factors that lead to MDOs and (3) determine the (potential) impact of the detected MDOs on patients.

Methods/Design

We plan to conduct a two-phase retrospective review of electronic health records in the Greater Manchester (GM) area of the UK. In the first phase, clinician reviewers will calibrate their performance in identifying and assessing MDOs against a gold standard ‘primary reviewer’ through the use of ‘double’ reviews of records. The findings will enable a preliminary estimate of the incidence of MDOs in general practice, which will be used to calculate the number of records to be reviewed in the second phase in order to estimate the true incidence of MDO in general practice. A sample of 15 general practices is required for phase 1 and up to 35 practices for phase 2. In each practice, the sample will consist of 100 patients aged ≥18 years on 1 April 2013 who have attended a face-to-face ‘index consultation’ between 1 April 2013 and 31 March 2015. The index consultation will be selected randomly from each unique patient record, occurring between 1 July 2013 and 30 June 2014.

Discussion

There are no reliable estimates of safety problems related to diagnosis in English general practice. This study will lay the foundation for safety improvements in this area by providing a more reliable estimate of MDOs, their impact and their contributory factors.

Electronic supplementary material

The online version of this article (doi:10.1186/s13012-015-0296-z) contains supplementary material, which is available to authorized users.

Diagnostic Errors in a PICU: Insights From the Morbidity and Mortality Conference

OBJECTIVES

To describe diagnostic errors identified among patients discussed at a PICU morbidity and mortality conference in terms of Goldman classification, medical category, severity, preventability, contributing factors, and occurrence in the diagnostic process.

DESIGN

Retrospective record review of morbidity and mortality conference agendas, patient charts, and autopsy reports.

SETTING

Single tertiary referral PICU in Baltimore, MD.

PATIENTS

Ninety-six patients discussed at the PICU morbidity and mortality conference from November 2011 to December 2012.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Eighty-nine of 96 patients (93%) discussed at the PICU morbidity and mortality conference had at least one identified safety event. A total of 377 safety events were identified. Twenty patients (21%) had identified misdiagnoses, comprising 5.3% of all safety events. Out of 20 total diagnostic errors identified, 35% were discovered at autopsy while 55% were reported primarily through the morbidity and mortality conference. Almost all diagnostic errors (95%) could have had an impact on patient survival or safety. Forty percent of errors did not cause actual patient harm, but 25% were severe enough to have potentially contributed to death (40% no harm vs 35% some harm vs 25% possibly contributed to death). Half of the diagnostic errors (50%) were rated as preventable. There were slightly more system-related factors (40%) solely contributing to diagnostic errors compared with cognitive factors (20%); however, 35% had both system and cognitive factors playing a role. Most errors involved vascular (35%) followed by neurologic (30%) events.

CONCLUSIONS

Diagnostic errors in the PICU are not uncommon and potentially cause patient harm. Most appear to be preventable by targeting both cognitive- and system-related contributing factors. Prospective studies are needed to further determine how and why diagnostic errors occur in the PICU and what interventions would likely be effective for prevention.

Assessing the potential adoption and usefulness of concurrent, action-oriented, electronic adverse drug event triggers designed for the outpatient setting

Background:

Adverse drug event (ADE) detection is an important priority for patient safety research. Trigger tools have been developed to help identify ADEs. In previous work we developed seven concurrent, action-oriented, electronic trigger algorithms designed to prompt clinicians to address ADEs in outpatient care.

Objectives:

We assessed the potential adoption and usefulness of the seven triggers by testing the positive predictive validity and obtaining stakeholder input.

Methods:

We adapted ADE triggers, “bone marrow toxin—white blood cell count (BMT-WBC),” “bone marrow toxin - platelet (BMT-platelet),” “potassium raisers,” “potassium reducers,” “creatinine,” “warfarin,” and “sedative hypnotics,” with logic to suppress flagging events with evidence of clinical intervention and applied the triggers to 50,145 patients from three large health care systems. Four pharmacists assessed trigger positive predictive value (PPV) with respect to ADE detection (conservatively excluding ADEs occurring during clinically appropriate care) and clinical usefulness (i.e., whether the trigger alert could change care to prevent harm). We measured agreement between raters using the free kappa and assessed positive PPV for the trigger’s detection of harm, clinical usefulness, and both. Stakeholders from the participating health care systems rated the likelihood of trigger adoption and the perceived ease of implementation.

Findings:

Agreement between pharmacist raters was moderately high for each ADE trigger (kappa free > 0.60). Trigger PPVs for harm ranged from 0 (Creatinine, BMT-WBC) to 17 percent (potassium raisers), while PPV for care change ranged from 0 (WBC) to 60 percent (Creatinine). Fifteen stakeholders rated the triggers. Our assessment identified five of the seven triggers as good candidates for implementation: Creatinine, BMT-Platelet, Potassium Raisers, Potassium Reducers, and Warfarin.

Conclusions:

At least five outpatient ADE triggers performed well and merit further evaluation in outpatient clinical care. When used in real time, these triggers may promote care changes to ameliorate patient harm.

Patient safety in primary health care: a systematic review

The aim of this study was to identify methodologies to evaluate incidents in primary health care, types of incidents, contributing factors, and solutions to make primary care safer. A systematic literature review was performed in the following databases: PubMed, Scopus, LILACS, SciELO, and Capes, from 2007 to 2012, in Portuguese, English, and Spanish. Thirty-three articles were selected: 26% on retrospective studies, 44% on prospective studies, including focus groups, questionnaires, and interviews, and 30% on cross-sectional studies. The most frequently used method was incident analysis from incident reporting systems (45%). The most frequent types of incidents in primary care were related to medication and diagnosis. The most relevant contributing factors were communication failures among member of the healthcare team. Research methods on patient safety in primary care are adequate and replicable, and they will likely be used more widely, thereby providing better knowledge on safety in this setting.

Clinical criteria to screen for inpatient diagnostic errors: a scoping review

Diagnostic errors are common and costly, but difficult to detect. "Trigger" tools have promise to facilitate detection, but have not been applied specifically for inpatient diagnostic error. We performed a scoping review to collate all individual "trigger" criteria that have been developed or validated that may indicate that an inpatient diagnostic error has occurred. We searched three databases and screened 8568 titles and abstracts to ultimately include 33 articles. We also developed a conceptual framework of diagnostic error outcomes using real clinical scenarios, and used it to categorize the extracted criteria. Of the multiple criteria we found related to inpatient diagnostic error and amenable to automated detection, the most common were death, transfer to a higher level of care, arrest or "code", and prolonged length of hospital stay. Several others, such as abrupt stoppage of multiple medications or change in procedure, may also be useful. Validation for general adverse event detection was done in 15 studies, but only one performed validation for diagnostic error specifically. Automated detection was used in only two studies. These criteria may be useful for developing diagnostic error detection tools.

Advancing the science of measurement of diagnostic errors in healthcare: the Safer Dx framework

Diagnostic errors are major contributors to harmful patient outcomes, yet they remain a relatively understudied and unmeasured area of patient safety. Although they are estimated to affect about 12 million Americans each year in ambulatory care settings alone, both the conceptual and pragmatic scientific foundation for their measurement is under-developed. Health care organizations do not have the tools and strategies to measure diagnostic safety and most have not integrated diagnostic error into their existing patient safety programs. Further progress toward reducing diagnostic errors will hinge on our ability to overcome measurement-related challenges. In order to lay a robust groundwork for measurement and monitoring techniques to ensure diagnostic safety, we recently developed a multifaceted framework to advance the science of measuring diagnostic errors (The Safer Dx framework). In this paper, we describe how the framework serves as a conceptual foundation for system-wide safety measurement, monitoring and improvement of diagnostic error. The framework accounts for the complex adaptive sociotechnical system in which diagnosis takes place (the structure), the distributed process dimensions in which diagnoses evolve beyond the doctor's visit (the process) and the outcomes of a correct and timely "safe diagnosis" as well as patient and health care outcomes (the outcomes). We posit that the Safer Dx framework can be used by a variety of stakeholders including researchers, clinicians, health care organizations and policymakers, to stimulate both retrospective and more proactive measurement of diagnostic errors. The feedback and learning that would result will help develop subsequent interventions that lead to safer diagnosis, improved value of health care delivery and improved patient outcomes.

Adding flexible temporal constraints to identify chronic comorbid conditions in ambulatory claims data

Chronic comorbid conditions are important predictors of primary care outcomes, provide context for clinical decisions, and are potential complications of diseases and treatments. Comorbidity indices and multimorbidity categorization strategies based on administrative claims data enumerate diagnostic codes in easily modifiable lists, but usually have inflexible temporal requirements, such as requiring two claims greater than 30 days apart, or three claims in three quarters. Table structures and claims data search algorithms were developed to support flexible temporal constraints. Tables of disease categories allow subgroups with different numbers of events, different times between similar claims, variable periods of interest, and specified diagnostic code substitutability. The strategy was tested on five years of private insurance claims from 2.2 million working age adults. The contrast between rarely recorded, high prevalence diagnoses (smoking and obesity) and frequently recorded but not necessarily chronic diagnoses (musculoskeletal complaints) demonstrated the advantage of flexible temporal criteria.

Making audit actionable: an example algorithm for blood pressure management in chronic kidney disease

Despite widespread use of clinical guidelines, actual care often falls short of ideal standards. Electronic health records (EHR) can be analyzed to provide information on how to improve care, but this is seldom done in sufficient detail to guide specific action. We developed an algorithm to provide practical, actionable information for care quality improvement using blood pressure (BP) management in chronic kidney disease (CKD) as an exemplar. We used UK clinical guidelines and EHR data from 440 patients in Salford (UK) to develop the algorithm. We then applied it to 532,409 individual patient records, identifying 11,097 CKD patients, 3,766 (34%) of which showed room for improvement in their care: either through medication optimization or better BP monitoring. Manual record reviews to evaluate accuracy indicated a positive-predictive value of 90%. Such algorithms could help improve the management of chronic conditions by providing the missing link between clinical audit and decision support.

Missed diagnosis of stroke in the emergency department: a cross-sectional analysis of a large population-based sample

BACKGROUND

Some cerebrovascular events are not diagnosed promptly, potentially resulting in death or disability from missed treatments. We sought to estimate the frequency of missed stroke and examine associations with patient, emergency department (ED), and hospital characteristics.

METHODS

Cross-sectional analysis using linked inpatient discharge and ED visit records from the 2009 Healthcare Cost and Utilization Project State Inpatient Databases and 2008-2009 State ED Databases across nine US states. We identified adult patients admitted for stroke with a treat-and-release ED visit in the prior 30 days, considering those given a non-cerebrovascular diagnosis as probable (benign headache or dizziness diagnosis) or potential (any other diagnosis) missed strokes.

RESULTS

There were 23,809 potential and 2243 probable missed strokes representing 12.7% and 1.2% of stroke admissions, respectively. Missed hemorrhages (n = 406) were linked to headache while missed ischemic strokes (n = 1435) and transient ischemic attacks (n = 402) were linked to headache or dizziness. Odds of a probable misdiagnosis were lower among men (OR 0.75), older individuals (18-44 years [base]; 45-64:OR 0.43; 65-74:OR 0.28; ≥ 75:OR 0.19), and Medicare (OR 0.66) or Medicaid (OR 0.70) recipients compared to privately insured patients. Odds were higher among Blacks (OR 1.18), Asian/Pacific Islanders (OR 1.29), and Hispanics (OR 1.30). Odds were higher in non-teaching hospitals (OR 1.45) and low-volume hospitals (OR 1.57).

CONCLUSIONS

We estimate 15,000-165,000 misdiagnosed cerebrovascular events annually in US EDs, disproportionately presenting with headache or dizziness. Physicians evaluating these symptoms should be particularly attuned to the possibility of stroke in younger, female, and non-White patients.

The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations

BACKGROUND

The frequency of outpatient diagnostic errors is challenging to determine due to varying error definitions and the need to review data across multiple providers and care settings over time. We estimated the frequency of diagnostic errors in the US adult population by synthesising data from three previous studies of clinic-based populations that used conceptually similar definitions of diagnostic error.

METHODS

Data sources included two previous studies that used electronic triggers, or algorithms, to detect unusual patterns of return visits after an initial primary care visit or lack of follow-up of abnormal clinical findings related to colorectal cancer, both suggestive of diagnostic errors. A third study examined consecutive cases of lung cancer. In all three studies, diagnostic errors were confirmed through chart review and defined as missed opportunities to make a timely or correct diagnosis based on available evidence. We extrapolated the frequency of diagnostic error obtained from our studies to the US adult population, using the primary care study to estimate rates of diagnostic error for acute conditions (and exacerbations of existing conditions) and the two cancer studies to conservatively estimate rates of missed diagnosis of colorectal and lung cancer (as proxies for other serious chronic conditions).

RESULTS

Combining estimates from the three studies yielded a rate of outpatient diagnostic errors of 5.08%, or approximately 12 million US adults every year. Based upon previous work, we estimate that about half of these errors could potentially be harmful.

CONCLUSIONS

Our population-based estimate suggests that diagnostic errors affect at least 1 in 20 US adults. This foundational evidence should encourage policymakers, healthcare organisations and researchers to start measuring and reducing diagnostic errors.

A unified conceptual model for diagnostic errors: underdiagnosis, overdiagnosis, and misdiagnosis

Progress in diagnostic error research has been hampered by a lack of unified terminology and definitions. This article proposes a novel framework for considering diagnostic errors, offering a unified conceptual model for underdiagnosis, overdiagnosis, and misdiagnosis. The model clarifies the critical separation between 'diagnostic process failures' (incorrect workups) and 'diagnosis label failures' (incorrect diagnoses). By dividing processes into those that are substandard, suboptimal, or optimal, important distinctions are drawn between 'preventable', 'reducible,' and 'unavoidable' diagnostic errors. The new model emphasizes the importance of mitigating diagnosis-related harms, regardless of whether the solutions require traditional safety strategies (preventable errors), more effective evidence dissemination (reducible errors; harms from overtesting and overdiagnosis), or new scientific discovery (currently unavoidable errors). Doing so maximizes our ability to prioritize solving various diagnosis-related problems from a societal value perspective. This model should serve as a foundation for developing consensus terminology and operationalized definitions for relevant diagnostic-error categories.

Diagnostic error: untapped potential for improving patient safety?

How many diagnostic errors occur? How often do errors cause harm, and how serious is that harm? Do we understand the major causes of diagnostic errors? Really, we don't know how to answer these questions! This article seeks to define a challenge facing all healthcare risk managers, whose usual methods of identifying and analyzing errors have not, and cannot, supply this missing information. What should risk managers do about diagnostic error? Our medical literature acknowledges the existence of a problem, but offers few practical solutions. This article will review some promising theories from the literature regarding how to identify and remediate diagnostic errors, and identify some tools and resources available to risk managers.

The incidence of diagnostic error in medicine

A wide variety of research studies suggest that breakdowns in the diagnostic process result in a staggering toll of harm and patient deaths. These include autopsy studies, case reviews, surveys of patient and physicians, voluntary reporting systems, using standardised patients, second reviews, diagnostic testing audits and closed claims reviews. Although these different approaches provide important information and unique insights regarding diagnostic errors, each has limitations and none is well suited to establishing the incidence of diagnostic error in actual practice, or the aggregate rate of error and harm. We argue that being able to measure the incidence of diagnostic error is essential to enable research studies on diagnostic error, and to initiate quality improvement projects aimed at reducing the risk of error and harm. Three approaches appear most promising in this regard: (1) using 'trigger tools' to identify from electronic health records cases at high risk for diagnostic error; (2) using standardised patients (secret shoppers) to study the rate of error in practice; (3) encouraging both patients and physicians to voluntarily report errors they encounter, and facilitating this process.

Advancing the research agenda for diagnostic error reduction

Diagnostic errors remain an underemphasised and understudied area of patient safety research. We briefly summarise the methods that have been used to conduct research on epidemiology, contributing factors and interventions related to diagnostic error and outline directions for future research. Research methods that have studied epidemiology of diagnostic error provide some estimate on diagnostic error rates. However, there appears to be a large variability in the reported rates due to the heterogeneity of definitions and study methods used. Thus, future methods should focus on obtaining more precise estimates in different settings of care. This would lay the foundation for measuring error rates over time to evaluate improvements. Research methods have studied contributing factors for diagnostic error in both naturalistic and experimental settings. Both approaches have revealed important and complementary information. Newer conceptual models from outside healthcare are needed to advance the depth and rigour of analysis of systems and cognitive insights of causes of error. While the literature has suggested many potentially fruitful interventions for reducing diagnostic errors, most have not been systematically evaluated and/or widely implemented in practice. Research is needed to study promising intervention areas such as enhanced patient involvement in diagnosis, improving diagnosis through the use of electronic tools and identification and reduction of specific diagnostic process ‘pitfalls’ (eg, failure to conduct appropriate diagnostic evaluation of a breast lump after a ‘normal’ mammogram). The last decade of research on diagnostic error has made promising steps and laid a foundation for more rigorous methods to advance the field.

Types and origins of diagnostic errors in primary care settings

IMPORTANCE

Diagnostic errors are an understudied aspect of ambulatory patient safety.

OBJECTIVES

To determine the types of diseases missed and the diagnostic processes involved in cases of confirmed diagnostic errors in primary care settings and to determine whether record reviews could shed light on potential contributory factors to inform future interventions.

DESIGN

We reviewed medical records of diagnostic errors detected at 2 sites through electronic health record-based triggers. Triggers were based on patterns of patients' unexpected return visits after an initial primary care index visit.

SETTING

A large urban Veterans Affairs facility and a large integrated private health care system.

PARTICIPANTS

Our study focused on 190 unique instances of diagnostic errors detected in primary care visits between October 1, 2006, and September 30, 2007.

MAIN OUTCOME MEASURES

Through medical record reviews, we collected data on presenting symptoms at the index visit, types of diagnoses missed, process breakdowns, potential contributory factors, and potential for harm from errors.

RESULTS

In 190 cases, a total of 68 unique diagnoses were missed. Most missed diagnoses were common conditions in primary care, with pneumonia (6.7%), decompensated congestive heart failure (5.7%), acute renal failure (5.3%), cancer (primary) (5.3%), and urinary tract infection or pyelonephritis (4.8%) being most common. Process breakdowns most frequently involved the patient-practitioner clinical encounter (78.9%) but were also related to referrals (19.5%), patient-related factors (16.3%), follow-up and tracking of diagnostic information (14.7%), and performance and interpretation of diagnostic tests (13.7%). A total of 43.7% of cases involved more than one of these processes. Patient-practitioner encounter breakdowns were primarily related to problems with history-taking (56.3%), examination (47.4%), and/or ordering diagnostic tests for further workup (57.4%). Most errors were associated with potential for moderate to severe harm.

CONCLUSIONS AND RELEVANCE

Diagnostic errors identified in our study involved a large variety of common diseases and had significant potential for harm. Most errors were related to process breakdowns in the patient-practitioner clinical encounter. Preventive interventions should target common contributory factors across diagnoses, especially those that involve data gathering and synthesis in the patient-practitioner encounter.