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

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.

How long is too long? A scoping review of health system delays in lung cancer

Earlier access to lung cancer specialist (LCS) care improves survival, highlighting the need for streamlined patient referral. International guidelines recommend 14-day maximum time intervals from general practitioner (GP) referral to first LCS appointment (“GP–LCS interval”), and diagnosis to treatment (“treatment interval”). We compared time intervals in lung cancer care against timeframe benchmarks, and explored barriers and facilitators to timely care.

We conducted a scoping review of literature from MEDLINE, Embase, Scopus and hand searches. Primary end-points were GP–LCS and treatment intervals. Performance against guidelines and factors responsible for delays were explored. We used descriptive statistics and nonparametric Wilcoxon rank sum tests to compare intervals in studies reporting fast-track interventions.

Of 1343 identified studies, 128 full-text articles were eligible. Only 33 (26%) studies reported GP–LCS intervals, with an overall median of 7 days and distributions largely meeting guidelines. Overall, 52 (41%) studies reported treatment intervals, with a median of 27 days, and distributions of times falling short of guidelines. There was no effect of fast-track interventions on reducing time intervals. Lack of symptoms and multiple procedures or specialist visits were suggested causes for delay.

Although most patients with lung cancer see a specialist within a reasonable timeframe, treatment commencement is often delayed. There is regional variation in establishing timeliness of care.

Delays to lung cancer care occur, especially in secondary care; variation in timeframe guidelines needs addressinghttp://ow.ly/hZt730kvKAb

Understanding Test Results Follow-Up in the Ambulatory Setting: Analysis of Multiple Perspectives

BACKGROUND

Delayed or incomplete test result follow-up, which can lead to missed and/or delayed diagnosis, is an important issue in the ambulatory setting. Delayed test result follow-up has been linked to poorer patient outcomes and increased risk of mortality and accounts for a large portion of medical malpractice claims. Yet improvements are difficult, reflecting the complexity of the test result follow-up process. Test result follow-up safety culture was investigated using qualitative and quantitative patient safety and quality of care data at an academic medical center.

METHODS

After an environmental scan, five sources of data were used to compass multiple perspectives on safety culture-two national surveys (AHRQ MO SOPS for safety culture and CG-CAHPS for patient satisfaction); patient and family complaints; safety reports; and provider response times to test message results in the electronic health record.

RESULTS

The following metrics were inspected: how patients and providers estimated the frequency for providing timely test results; how patients' satisfaction with their provider correlated with their provider's response time to test result messages; and qualitative themes in patient complaints and safety reports filed by clinic. The institution was compared to national benchmarks using surveys. As test result response time decreased, patient satisfaction increased (p = 0.0073).

CONCLUSION

Test result follow-up culture was investigated using tools typically used to examine patient satisfaction and experience and staff culture. Use of these five sources of data led to an examination of multiple perspectives in follow-up culture and identification of possible explanations for inappropriate follow-up. These data sources can be further explored to identify possible solutions.

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.

Responsibility for follow-up during the diagnostic process in primary care: a secondary analysis of International Cancer Benchmarking Partnership data

BACKGROUND

It is unclear to what extent primary care practitioners (PCPs) should retain responsibility for follow-up to ensure that patients are monitored until their symptoms or signs are explained.

AIM

To explore the extent to which PCPs retain responsibility for diagnostic follow-up actions across 11 international jurisdictions.

DESIGN AND SETTING

A secondary analysis of survey data from the International Cancer Benchmarking Partnership.

METHOD

The authors counted the proportion of 2879 PCPs who retained responsibility for each area of follow-up (appointments, test results, and non-attenders). Proportions were weighted by the sample size of each jurisdiction. Pooled estimates were obtained using a random-effects model, and UK estimates were compared with non-UK ones. Free-text responses were analysed to contextualise quantitative findings using a modified grounded theory approach.

RESULTS

PCPs varied in their retention of responsibility for follow-up from 19% to 97% across jurisdictions and area of follow-up. Test reconciliation was inadequate in most jurisdictions. Significantly fewer UK PCPs retained responsibility for test result communication (73% versus 85%, P = 0.04) and non-attender follow-up (78% versus 93%, P<0.01) compared with non-UK PCPs. PCPs have developed bespoke, inconsistent solutions to follow-up. In cases of greatest concern, 'double safety netting' is described, where both patient and PCP retain responsibility.

CONCLUSION

The degree to which PCPs retain responsibility for follow-up is dependent on their level of concern about the patient and their primary care system's properties. Integrated systems to support follow-up are at present underutilised, and research into their development, uptake, and effectiveness seems warranted.

Electronic Triggers to Identify Delays in Follow-Up of Mammography: Harnessing the Power of Big Data in Health Care

PURPOSE

We previously developed electronic triggers to automatically flag records for patients experiencing potential delays in diagnostic evaluation for certain cancers. Because of the unique clinical, logistic, and legal aspects of mammography, this study was conducted to evaluate the effectiveness of a trigger to flag delayed follow-up on mammography.

METHODS

An algorithm was developed to detect delays in follow-up of abnormal mammographic results (>60 days for BI-RADS^® 0, 4, and 5 and >7 months for BI-RADS 3) using clinical data in the electronic health record. Flagged records were then manually reviewed to determine the trigger's performance characteristics (positive and negative predictive value, sensitivity, and specificity). The frequency of delays and patient communication related to abnormal results, reasons for lack of follow-up, and whether patients were subsequently diagnosed with breast cancer were also assessed.

RESULTS

Of 365,686 patients seen between January 1, 2010, and May 31, 2015, the trigger identified 2,129 patients with abnormal findings on mammography, of whom it flagged 552 as having delays in follow-up. From these, review of 400 randomly selected records revealed 283 true delays (positive predictive value, 71%; 95% confidence interval, 66%-75%), including 280 records without any documented plan and three patients with plans that were not adhered to. Transcription and reporting inconsistencies were identified in 27% of externally performed mammographic reports. Only 335 records (84%) contained specific documentation that the patient was informed of the abnormal result.

CONCLUSIONS

Care delays appear to continue despite federal laws requiring patient notification of mammographic results within 30 days. Clinical application of mammography-related triggers could help detect these delays.

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.

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.

Interventions to Improve Follow-up of Positive Results on Fecal Blood Tests: A Systematic Review

BACKGROUND

Fecal immunochemical testing is the most commonly used method for colorectal cancer screening worldwide. However, its effectiveness is frequently undermined by failure to obtain follow-up colonoscopy after positive test results.

PURPOSE

To evaluate interventions to improve rates of follow-up colonoscopy for adults after a positive result on a fecal test (guaiac or immunochemical).

DATA SOURCES

English-language studies from the Cochrane Central Register of Controlled Trials, PubMed, and Embase from database inception through June 2017.

STUDY SELECTION

Randomized and nonrandomized studies reporting an intervention for colonoscopy follow-up of asymptomatic adults with positive fecal test results.

DATA EXTRACTION

Two reviewers independently extracted data and ranked study quality; 2 rated overall strength of evidence for each category of study type.

DATA SYNTHESIS

Twenty-three studies were eligible for analysis, including 7 randomized and 16 nonrandomized studies. Three were at low risk of bias. Eleven studies described patient-level interventions (changes to invitation, provision of results or follow-up appointments, and patient navigators), 5 provider-level interventions (reminders or performance data), and 7 system-level interventions (automated referral, precolonoscopy telephone calls, patient registries, and quality improvement efforts). Moderate evidence supported patient navigators and provider reminders or performance data. Evidence for system-level interventions was low. Seventeen studies reported the proportion of test-positive patients who completed colonoscopy compared with a control population, with absolute differences of -7.4 percentage points (95% CI, -19 to 4.3 percentage points) to 25 percentage points (CI, 14 to 35 percentage points).

LIMITATION

More than half of studies were at high or very high risk of bias; heterogeneous study designs and characteristics precluded meta-analysis.

CONCLUSION

Patient navigators and giving providers reminders or performance data may help improve colonoscopy rates of asymptomatic adults with positive fecal blood test results. Current evidence about useful system-level interventions is scant and insufficient.

PRIMARY FUNDING SOURCE

National Cancer Institute. (PROSPERO: CRD42016048286).

An electronic trigger based on care escalation to identify preventable adverse events in hospitalised patients

Background

Methods to identify preventable adverse events typically have low yield and efficiency. We refined the methods of Institute of Healthcare Improvement’s Global Trigger Tool (GTT) application and leveraged electronic health record (EHR) data to improve detection of preventable adverse events, including diagnostic errors.

Methods

We queried the EHR data repository of a large health system to identify an ‘index hospitalization’ associated with care escalation (defined as transfer to the intensive care unit (ICU) or initiation of rapid response team (RRT) within 15 days of admission) between March 2010 and August 2015. To enrich the record review sample with unexpected events, we used EHR clinical data to modify the GTT algorithm and limited eligible patients to those at lower risk for care escalation based on younger age and presence of minimal comorbid conditions. We modified the GTT review methodology; two physicians independently reviewed eligible ‘e-trigger’ positive records to identify preventable diagnostic and care management events.

Results

Of 88 428 hospitalisations, 887 were associated with care escalation (712 ICU transfers and 175 RRTs), of which 92 were flagged as trigger-positive and reviewed. Preventable adverse events were detected in 41 cases, yielding a trigger positive predictive value of 44.6% (reviewer agreement 79.35%; Cohen’s kappa 0.573). We identified 7 (7.6%) diagnostic errors and 34 (37.0%) care management-related events: 24 (26.1%) adverse drug events, 4 (4.3%) patient falls, 4 (4.3%) procedure-related complications and 2 (2.2%) hospital-associated infections. In most events (73.1%), there was potential for temporary harm.

Conclusion

We developed an approach using an EHR data-based trigger and modified review process to efficiently identify hospitalised patients with preventable adverse events, including diagnostic errors. Such e-triggers can help overcome limitations of currently available methods to detect preventable harm in hospitalised patients.

Variation in interoperability across clinical laboratories nationwide

OBJECTIVE

To characterize nationwide variation and factors associated with clinical laboratories': (1) capabilities to send structured test results electronically to ordering practitioners' EHR systems; and (2) their levels of exchange activity, as measured by whether they sent more than three-quarters of their test results as structured data to ordering practitioners' EHR systems.

MATERIALS AND METHODS

A national survey of all independent and hospital laboratories was conducted in 2013. Using an analytic weighted sample of 9382 clinical laboratories, a series of logistic regression analyses were conducted to identify organizational and area characteristics associated with clinical laboratories' exchange capability and activity.

RESULTS

Hospital-based clinical laboratories (71%) and larger clinical laboratories (80%) had significantly higher levels of capability compared to independent (58%) and smaller laboratories (48%), respectively; though all had similar levels of exchange activity, with 30% of clinical laboratories sending 75% or more of their test results electronically. In multivariate analyses, hospital and the largest laboratories had 1.87 and 4.40 higher odds, respectively, of possessing the capability to send results electronically compared to independent laboratories (p<0.001). Laboratories located in areas with a higher share of potential exchange partners had a small but significantly greater capability to send results electronically and higher levels of exchange activity(p<0.05).

CONCLUSION

Clinical laboratories' capability to exchange varied by size and type; however, all clinical laboratories had relatively low levels of exchange activity. The role of exchange partners potentially played a small but significant role in driving exchange capability and activity.

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.

Electronic Detection of Delayed Test Result Follow-Up in Patients with Hypothyroidism

BACKGROUND

Delays in following up abnormal test results are a common problem in outpatient settings. Surveillance systems that use trigger tools to identify delayed follow-up can help reduce missed opportunities in care.

OBJECTIVE

To develop and test an electronic health record (EHR)-based trigger algorithm to identify instances of delayed follow-up of abnormal thyroid-stimulating hormone (TSH) results in patients being treated for hypothyroidism.

DESIGN

We developed an algorithm using structured EHR data to identify patients with hypothyroidism who had delayed follow-up (>60 days) after an abnormal TSH. We then retrospectively applied the algorithm to a large EHR data warehouse within the Department of Veterans Affairs (VA), on patient records from two large VA networks for the period from January 1, 2011, to December 31, 2011. Identified records were reviewed to confirm the presence of delays in follow-up.

KEY RESULTS

During the study period, 645,555 patients were seen in the outpatient setting within the two networks. Of 293,554 patients with at least one TSH test result, the trigger identified 1250 patients on treatment for hypothyroidism with elevated TSH. Of these patients, 271 were flagged as potentially having delayed follow-up of their test result. Chart reviews confirmed delays in 163 of the 271 flagged patients (PPV = 60.1%).

CONCLUSIONS

An automated trigger algorithm applied to records in a large EHR data warehouse identified patients with hypothyroidism with potential delays in thyroid function test results follow-up. Future prospective application of the TSH trigger algorithm can be used by clinical teams as a surveillance and quality improvement technique to monitor and improve follow-up.

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.

Application of Electronic Algorithms to Improve Diagnostic Evaluation for Bladder Cancer

BACKGROUND

Strategies to ensure timely diagnostic evaluation of hematuria are needed to reduce delays in bladder cancer diagnosis.

OBJECTIVE

To evaluate the performance of electronic trigger algorithms to detect delays in hematuria follow-up.

METHODS

We developed a computerized trigger to detect delayed follow-up action on a urinalysis result with high-grade hematuria (>50 red blood cells/high powered field). The trigger scanned clinical data within a Department of Veterans Affairs (VA) national data repository to identify all patient records with hematuria, then excluded those where follow-up was unnecessary (e.g., terminal illness) or where typical follow-up action was detected (e.g., cystoscopy). We manually reviewed a randomly-selected sample of flagged records to confirm delays. We performed a similar analysis of records with hematuria that were marked as not delayed (non-triggered). We used review findings to calculate trigger performance.

RESULTS

Of 310,331 patients seen between 1/1/2012-12/31/2014, the trigger identified 5,857 patients who experienced high-grade hematuria, of which 495 experienced a delay. On manual review of 400 randomly-selected triggered records and 100 non-triggered records, the trigger achieved positive and negative predictive values of 58% and 97%, respectively.

CONCLUSIONS

Triggers offer a promising method to detect delays in care of patients with high-grade hematuria and warrant further evaluation in clinical practice as a means to reduce delays in bladder cancer diagnosis.

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.