scholarly journals Controversies in diagnosis: contemporary debates in the diagnostic safety literature

Diagnosis ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Paul A. Bergl ◽  
Thilan P. Wijesekera ◽  
Najlla Nassery ◽  
Karen S. Cosby
Keyword(s):  
Health Care ◽  
Patient Safety ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Healthcare Research ◽  
Washington Dc ◽  
Approaches To Teaching ◽  
Key Topics ◽  
Medical Educators ◽  
Safety First

AbstractSince the 2015 publication of the National Academy of Medicine’s (NAM) Improving Diagnosis in Health Care (Improving Diagnosis in Health Care. In: Balogh EP, Miller BT, Ball JR, editors. Improving Diagnosis in Health Care. Washington (DC): National Academies Press, 2015.), literature in diagnostic safety has grown rapidly. This update was presented at the annual international meeting of the Society to Improve Diagnosis in Medicine (SIDM). We focused our literature search on articles published between 2016 and 2018 using keywords in Pubmed and the Agency for Healthcare Research and Quality (AHRQ)’s Patient Safety Network’s running bibliography of diagnostic error literature (Diagnostic Errors Patient Safety Network: Agency for Healthcare Research and Quality; Available from: https://psnet.ahrq.gov/search?topic=Diagnostic-Errors&f_topicIDs=407). Three key topics emerged from our review of recent abstracts in diagnostic safety. First, definitions of diagnostic error and related concepts are evolving since the NAM’s report. Second, medical educators are grappling with new approaches to teaching clinical reasoning and diagnosis. Finally, the potential of artificial intelligence (AI) to advance diagnostic excellence is coming to fruition. Here we present contemporary debates around these three topics in a pro/con format.

Lessons in clinical reasoning – pitfalls, myths, and pearls: the contribution of faulty data gathering and synthesis to diagnostic error

Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Martin A. Schaller-Paule ◽  
Helmuth Steinmetz ◽  
Friederike S. Vollmer ◽  
Melissa Plesac ◽  
Felix Wicke ◽  
...  
Keyword(s):  
Patient Safety ◽  
Clinical Reasoning ◽  
Data Gathering ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Care Physician ◽  
Diagnostic Process ◽  
Myasthenic Crisis ◽  
Hospital Organization ◽  
Substantial Impact

Abstract Objectives Errors in clinical reasoning are a major factor for delayed or flawed diagnoses and put patient safety at risk. The diagnostic process is highly dependent on dynamic team factors, local hospital organization structure and culture, and cognitive factors. In everyday decision-making, physicians engage that challenge partly by relying on heuristics – subconscious mental short-cuts that are based on intuition and experience. Without structural corrective mechanisms, clinical judgement under time pressure creates space for harms resulting from systems and cognitive errors. Based on a case-example, we outline different pitfalls and provide strategies aimed at reducing diagnostic errors in health care. Case presentation A 67-year-old male patient was referred to the neurology department by his primary-care physician with the diagnosis of exacerbation of known myasthenia gravis. He reported shortness of breath and generalized weakness, but no other symptoms. Diagnosis of respiratory distress due to a myasthenic crisis was made and immunosuppressive therapy and pyridostigmine were given and plasmapheresis was performed without clinical improvement. Two weeks into the hospital stay, the patient’s dyspnea worsened. A CT scan revealed extensive segmental and subsegmental pulmonary emboli. Conclusions Faulty data gathering and flawed data synthesis are major drivers of diagnostic errors. While there is limited evidence for individual debiasing strategies, improving team factors and structural conditions can have substantial impact on the extent of diagnostic errors. Healthcare organizations should provide the structural supports to address errors and promote a constructive culture of patient safety.

scholarly journals Serious misdiagnosis-related harms in malpractice claims: The “Big Three” – vascular events, infections, and cancers

Diagnosis ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 227-240 ◽  
Author(s):  
David E. Newman-Toker ◽  
Adam C. Schaffer ◽  
C. Winnie Yu-Moe ◽  
Najlla Nassery ◽  
Ali S. Saber Tehrani ◽  
...  
Keyword(s):  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Healthcare Research ◽  
Insurance Company ◽  
Vascular Events ◽  
Cross Sectional ◽  
Permanent Disability ◽  
High Severity ◽  
Malpractice Claims ◽  
Severity Of Injury

Abstract Background Diagnostic errors cause substantial preventable harm, but national estimates vary widely from 40,000 to 4 million annually. This cross-sectional analysis of a large medical malpractice claims database was the first phase of a three-phase project to estimate the US burden of serious misdiagnosis-related harms. Methods We sought to identify diseases accounting for the majority of serious misdiagnosis-related harms (morbidity/mortality). Diagnostic error cases were identified from Controlled Risk Insurance Company (CRICO)’s Comparative Benchmarking System (CBS) database (2006–2015), representing 28.7% of all US malpractice claims. Diseases were grouped according to the Agency for Healthcare Research and Quality (AHRQ) Clinical Classifications Software (CCS) that aggregates the International Classification of Diseases diagnostic codes into clinically sensible groupings. We analyzed vascular events, infections, and cancers (the “Big Three”), including frequency, severity, and settings. High-severity (serious) harms were defined by scores of 6–9 (serious, permanent disability, or death) on the National Association of Insurance Commissioners (NAIC) Severity of Injury Scale. Results From 55,377 closed claims, we analyzed 11,592 diagnostic error cases [median age 49, interquartile range (IQR) 36–60; 51.7% female]. These included 7379 with high-severity harms (53.0% death). The Big Three diseases accounted for 74.1% of high-severity cases (vascular events 22.8%, infections 13.5%, and cancers 37.8%). In aggregate, the top five from each category (n = 15 diseases) accounted for 47.1% of high-severity cases. The most frequent disease in each category, respectively, was stroke, sepsis, and lung cancer. Causes were disproportionately clinical judgment factors (85.7%) across categories (range 82.0–88.8%). Conclusions The Big Three diseases account for about three-fourths of serious misdiagnosis-related harms. Initial efforts to improve diagnosis should focus on vascular events, infections, and cancers.

scholarly journals Five things to know about diagnostic error

Diagnosis ◽  
2017 ◽  
Vol 4 (1) ◽  
pp. 13-15 ◽  
Author(s):  
Darya Yermak ◽  
Peter Cram ◽  
Janice L. Kwan
Keyword(s):  
Patient Safety ◽  
Information Technologies ◽  
Safety Issue ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Cognitive Errors ◽  
Health Information Technologies ◽  
Education And Health ◽  
Patient Safety Issue ◽  
Missed Diagnoses

AbstractDiagnostic error represents an important patient safety issue. Herein, we summarize five important things to know about this topic. (1) At least 1 in 20 adults are affected by diagnostic errors annually. (2) The root causes for diagnostic errors are typically multifactorial. (3) Cognitive errors are found in the majority of cases. (4) Most missed diagnoses involve common conditions. (5) Advancements in policy, education, and health information technologies hold promise for improving diagnostic safety.

scholarly journals The challenges in defining and measuring diagnostic error

Diagnosis ◽  
2015 ◽  
Vol 2 (2) ◽  
pp. 97-103 ◽  
Author(s):  
Laura Zwaan ◽  
Hardeep Singh
Keyword(s):  
Patient Safety ◽  
Real World ◽  
Expert Panel ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Disease Diagnosis ◽  
International Conference ◽  
Research Challenges ◽  
Conducting Research ◽  
Over Time

AbstractDiagnostic errors have emerged as a serious patient safety problem but they are hard to detect and complex to define. At the research summit of the 2013 Diagnostic Error in Medicine 6th International Conference, we convened a multidisciplinary expert panel to discuss challenges in defining and measuring diagnostic errors in real-world settings. In this paper, we synthesize these discussions and outline key research challenges in operationalizing the definition and measurement of diagnostic error. Some of these challenges include 1) difficulties in determining error when the disease or diagnosis is evolving over time and in different care settings, 2) accounting for a balance between underdiagnosis and overaggressive diagnostic pursuits, and 3) determining disease diagnosis likelihood and severity in hindsight. We also build on these discussions to describe how some of these challenges can be addressed while conducting research on measuring diagnostic error.

scholarly journals Recommendations for using the Revised Safer Dx Instrument to help measure and improve diagnostic safety

Diagnosis ◽  
2019 ◽  
Vol 6 (4) ◽  
pp. 315-323 ◽  
Author(s):  
Hardeep Singh ◽  
Arushi Khanna ◽  
Christiane Spitzmueller ◽  
Ashley N.D. Meyer
Keyword(s):  
Health Care ◽  
Data Collection ◽  
Medical Record ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Diagnostic Process ◽  
Sources Of Information ◽  
Missed Opportunities ◽  
Health Care Settings ◽  
Data Collection Instrument

Abstract The medical record continues to be one of the most useful and accessible sources of information to examine the diagnostic process. However, medical record review studies of diagnostic errors have often used subjective judgments and found low inter-rater agreement among reviewers when determining the presence or absence of diagnostic error. In our previous work, we developed a structured data-collection instrument, called the Safer Dx Instrument, consisting of objective criteria to improve the accuracy of assessing diagnostic errors in primary care. This paper proposes recommendations on how clinicians and health care organizations could use the Revised Safer Dx Instrument in identifying and understanding missed opportunities to make correct and timely diagnoses. The instrument revisions addressed both methodological and implementation issues identified during initial use and included refinements to the instrument to allow broader application across all health care settings. In addition to leveraging knowledge from piloting the instrument in several health care settings, we gained insights from multiple researchers who had used the instrument in studies involving emergency care, inpatient care and intensive care unit settings. This allowed us to enhance and extend the scope of this previously validated data collection instrument. In this paper, we describe the refinement process and provide recommendations for application and use of the Revised Safer Dx Instrument across a broad range of health care settings. The instrument can help users identify potential diagnostic errors in a standardized way for further analysis and safety improvement efforts as well as provide data for clinician feedback and reflection. With wider adoption and use by clinicians and health systems, the Revised Safer Dx Instrument could help propel the science of measuring and reducing diagnostic errors forward.

scholarly journals 1070. Handshake Antimicrobial Stewardship as a Model to Prevent Patient Safety Incidents and Recognize Diagnostic Errors

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S379-S379
Author(s):  
Justin B Searns ◽  
Manon Williams ◽  
Christine MacBrayne ◽  
Ann Wirtz ◽  
Sarah Parker ◽  
...  
Keyword(s):  
Patient Safety ◽  
Antimicrobial Stewardship ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Care Patient ◽  
Patient Harm ◽  
Administration Error ◽  
Category Labels ◽  
The Impact ◽  
Patient Safety Incidents

Abstract Background Patient safety incidents (PSIs), such as diagnostic errors, are common events that may lead to significant patient harm. Few studies describe the impact that antimicrobial stewardship programs (ASPs) have preventing PSIs and recognizing diagnostic errors. Handshake Stewardship has emerged as a specific ASP model that involves prospective review of hospital-wide antimicrobial ordering with a compressed “second look” of relevant clinical and historical patient data. In person recommendations are then provided directly to the medical team. The objective of this project was to evaluate the potential impact that Handshake Stewardship has on preventing PSIs and recognizing diagnostic errors. Methods Following Children’s Hospital Colorado (CHCO) ASP’s implementation of the Handshake Stewardship model in October 2013, the CHCO ASP team began prospectively self-labeling interventions as “Great Catches” (GCs). These GCs were defined as any ASP intervention that “notably changed the trajectory of patient care.” Patient charts for all GCs from October 2014 through May 2018 were retrospectively reviewed and each intervention was assigned one or more descriptive category labels including: administration error, de-escalation/escalation of therapy, bug-drug mismatch, inappropriate dose/duration, potential adverse effect, alternative diagnosis, additional testing, prevent hospital admission, and epidemiology alerts. In addition, each intervention was scored using the previously validated “Safer Dx Instrument” to determine which GCs intervened on a potential diagnostic error. Results From October 2014 through May 2018 there were 87,322 admissions to CHCO. Our ASP team intervened on 6,735/87,322 (7.7%) of these admissions. Of these, 174/6,735 (2.6%) were prospectively labeled by ASP providers as GCs, of which 44/174 (25%) resulted in new infectious disease consultations. Conclusion Given the frequency and significance of PSIs including diagnostic error, systems are needed to help recognize and prevent patient harm. The Handshake Stewardship model may help prevent PSIs and recognize diagnostic errors among hospitalized children. Disclosures All authors: No reported disclosures.

IDEA4PS: The Development of a Research-Oriented Learning Healthcare System

2018 ◽  
Vol 33 (4) ◽  
pp. 420-425
Author(s):  
Susan Moffatt-Bruce ◽  
Timothy Huerta ◽  
Alice Gaughan ◽  
Ann Scheck McAlearney
Keyword(s):  
Health Care ◽  
Patient Safety ◽  
Healthcare System ◽  
Care Delivery ◽  
Medical Center ◽  
Academic Medical Center ◽  
Healthcare Research ◽  
Delivery Of Care ◽  
Rigorous Research ◽  
Learning Healthcare System

Leveraging opportunities to learn and then improve the delivery of care using experiences throughout the health care system is essential in efforts to transform health care delivery. The authors present the approach of one academic medical center in becoming a research-oriented Learning Healthcare System (ro-LHS). By reframing the role of research in improving outcomes, the organization was able to move beyond its focus on quality improvement to foster a culture in which feedback informs practice and research drives improvement. The patient safety learning laboratory, the Institute for the Design of Environments Aligned for Patient Safety, funded by the Agency for Healthcare Research and Quality, has provided foundational infrastructure to connect stakeholders across the medical center and university and conduct rigorous research in the context of practice. With this new focus, research now informs operations in a cycle of continuous improvement across the authors’ ro-LHS.

Use of a novel, modified fishbone diagram to analyze diagnostic errors

Diagnosis ◽  
2014 ◽  
Vol 1 (2) ◽  
pp. 167-171 ◽  
Author(s):  
James B. Reilly ◽  
Jennifer S. Myers ◽  
Doug Salvador ◽  
Robert L. Trowbridge
Keyword(s):  
Patient Safety ◽  
Medical Errors ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Root Cause ◽  
Medical Centers ◽  
Cognitive Framework ◽  
Fishbone Diagram ◽  
Complex Events ◽  
Individual Cognition

AbstractDiagnostic errors comprise a critical subset of medical errors and often stem from errors in individual cognition. While traditional patient safety methods for dissecting medical errors focus on faulty systems, such methods are often less useful in cases of diagnostic error, and a broader cognitive framework is needed to ensure a comprehensive analysis of these complex events. The fishbone diagram is a widely utilized patient safety tool that helps to facilitate root cause analysis discussions. This tool was expanded by the authors to reflect the contributions of both systems and individual cognitive errors to diagnostic errors. We describe how two medical centers have applied this modified fishbone diagram to approach diagnostic errors in a way that better meets the patient safety and educational needs of their respective institutions.

scholarly journals Assessment of patient safety culture in a rural tertiary health care hospital of Central India

2018 ◽  
Vol 5 (7) ◽  
pp. 2791
Author(s):  
R. C. Goyal ◽  
Sonali Choudhari
Keyword(s):  
Health Care ◽  
Patient Safety ◽  
Safety Culture ◽  
Low Income ◽  
Central India ◽  
Patient Safety Culture ◽  
Clinical Work ◽  
Healthcare Research ◽  
Care Hospital ◽  
Management Support

Background: A safety culture assessment provides an organization with a basic understanding of the safety related perceptions and attitudes of its managers and staff. While patient safety has been a major area of research in industrialized nations for over a decade, data on the root causes of unsafe care in low-income settings is sparse. The objective of the study was to assess the patient safety culture in a rural tertiary health care hospital situated in Central India.Methods: A survey conducted during year 2015, in a rural tertiary health care teaching hospital, Maharashtra (India). The study participants were the 156 hospital staff working in various clinical work areas. The agency for healthcare research and quality hospital survey on patient safety culture, a validated instrument is used as an assessment tool.Results: Total 144 participants included in the study, 75 (52%) were females and rest were males 48%. Out of these 111 (77), maximum number of staff (57.05%) was belonging to different intensive care units.  57% of participants had worked in the hospital for 1 to 5 years.  For the unit level safety culture dimension, the maximum composite score of positive responses was obtained for “Organizational learning- continuous improvement” (67%) followed by “Hospital management support for patient safety” (65%).  On the other hand only 48% survey participants gave an affirmative opinion with respect to “Feedback and communication about error”. For the hospital wide dimensions response rate was obtained as 62% for the “Teamwork across Hospital Units” while for the dimension “Hospital Handoffs & Transitions”, the score came out as 55%.Conclusions: The perception of patient safety and standards of patient safety were fairly good in the present rural tertiary health care hospital, but there is an ample of prospect in improvement with regard to event reporting, feedback and non punitive error.

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