Gordon D. Schiff

Diagnostic Error in Medicine: Analysis of 583 Physician-Reported Errors

BACKGROUND Missed or delayed diagnoses are a common but understudied area in patient safety research. To better understand the types, causes, and prevention of such errors, we surveyed clinicians to solicit perceived cases of missed and delayed diagnoses. METHODS A 6-item written survey was administered at 20 grand rounds presentations across the United States and by mail at 2 collaborating institutions. Respondents were asked to report 3 cases of diagnostic errors and to describe their perceived causes, seriousness, and frequency. RESULTS A total of 669 cases were reported by 310 clinicians from 22 institutions. After cases without diagnostic errors or lacking sufficient details were excluded, 583 remained. Of these, 162 errors (28%) were rated as major, 241 (41%) as moderate, and 180 (31%) as minor or insignificant. The most common missed or delayed diagnoses were pulmonary embolism (26 cases [4.5% of total]), drug reactions or overdose (26 cases [4.5%]), lung cancer (23 cases [3.9%]), colorectal cancer (19 cases [3.3%]), acute coronary syndrome (18 cases [3.1%]), breast cancer (18 cases [3.1%]), and stroke (15 cases [2.6%]). Errors occurred most frequently in the testing phase (failure to order, report, and follow-up laboratory results) (44%), followed by clinician assessment errors (failure to consider and overweighing competing diagnosis) (32%), history taking (10%), physical examination (10%), and referral or consultation errors and delays (3%). CONCLUSIONS Physicians readily recalled multiple cases of diagnostic errors and were willing to share their experiences. Using a new taxonomy tool and aggregating cases by diagnosis and error type revealed patterns of diagnostic failures that suggested areas for improvement. Systematic solicitation and analysis of such errors can identify potential preventive strategies.

Decompensated heart failure: Symptoms, patterns of onset, and contributing factors

Abstract Purpose To examine the time course, contributing factors, and patient responses to decompensated heart failure. Methods We studied consecutive patients admitted to a public general hospital with a diagnosis of heart failure. Using a timeline follow-back technique, a nurse interviewer administered a questionnaire shortly after admission, exploring knowledge of a heart failure diagnosis, the symptoms and time course of decompensation, and patient responses to worsening symptoms. Results Of 87 patients, 83 (95%) consented to be interviewed. Only 49 (59%) were aware of the diagnosis of heart failure. Symptoms associated with decompensation included dyspnea in 81 patients (98%), edema in 64 patients (77%), and weight gain in 34 patients (41%). Onset of worsening of these symptoms was noted a mean (± SD) of 12.4 ±1.4 days before admission for edema, 11.3 ±1.6 days for weight gain, and 8.4 ± 0.9 days for dyspnea. Forty-two patients (57%) reported missing or skipping medication because of various factors, particularly missed outpatient appointments. Conclusion Using a timeline follow-back interview, we identified a period of days to weeks between the onset of worsening symptoms and hospital admission for heart failure decompensation. This pattern suggests that there is a time window between symptom exacerbation and admission during which earlier access and intervention might prevent hospitalization in these patients. Medication lapses continue to be an important preventable cause of decompensation leading to admission.

Can Electronic Clinical Documentation Help Prevent Diagnostic Errors

Drs. Gordon Schiff and David Bates envision a redesigned electronic documentation function that anticipates new approaches to improving diagnosis.

Beyond the Prescription: Medication Monitoring and Adverse Drug Events in Older Adults

Whether a person will suffer harm from a medication or how severe that harm will be is difficult to predict precisely. As a result, many adverse drug events (ADEs) occur in patients in whom it was reasonable to believe that the drugs benefits exceeded its risks. Improving safety and reducing the burden of ADEs in older adults requires addressing this uncertainty by not only focusing on the appropriateness of the initial prescribing decision, but also by detecting and mitigating adverse events once they have started to occur. Such enhanced monitoring of signs, symptoms, and laboratory parameters can determine whether an adverse event has only mild and short‐term consequences or major long‐term effects on morbidity and mortality. Although current medication monitoring practices are often suboptimal, several strategies can be leveraged to improve the quality and outcomes of monitoring. These strategies include using health information technology to link pharmacy and laboratory data, prospective delineation of risk, and patient outreach and activation, all within a framework of team‐based approaches to patient management. Although many of these strategies are theoretically possible now, they are poorly used and will be difficult to implement without a significant restructuring of medical practice. An enhanced focus on medication monitoring will also require a new conceptual framework to re‐engineer the prescribing process. With this approach, prescribing quality does not hinge on static attributes of the initial prescribing decision but entails a dynamic process in which the benefits and harms of drugs are actively monitored, managed, and reassessed over time.

Interface design principles for usable decision support

Developing effective clinical decision support (CDS) systems for the highly complex and dynamic domain of clinical medicine is a serious challenge for designers. Poor usability is one of the core barriers to adoption and a deterrent to its routine use. We reviewed reports describing system implementation efforts and collected best available design conventions, procedures, practices and lessons learned in order to provide developers a short compendium of design goals and recommended principles. This targeted review is focused on CDS related to medication prescribing. Published reports suggest that important principles include consistency of design concepts across networked systems, use of appropriate visual representation of clinical data, use of controlled terminology, presenting advice at the time and place of decision making and matching the most appropriate CDS interventions to clinical goals. Specificity and contextual relevance can be increased by periodic review of trigger rules, analysis of performance logs and maintenance of accurate allergy, problem and medication lists in health records in order to help avoid excessive alerting. Developers need to adopt design practices that include user-centered, iterative design and common standards based on human-computer interaction (HCI) research methods rooted in ethnography and cognitive science. Suggestions outlined in this report may help clarify the goals of optimal CDS design but larger national initiatives are needed for systematic application of human factors in health information technology (HIT) development. Appropriate design strategies are essential for developing meaningful decision support systems that meet the grand challenges of high-quality healthcare.

Methodological Review: Interface design principles for usable decision support: A targeted review of best practices for clinical prescribing interventions

Developing effective clinical decision support (CDS) systems for the highly complex and dynamic domain of clinical medicine is a serious challenge for designers. Poor usability is one of the core barriers to adoption and a deterrent to its routine use. We reviewed reports describing system implementation efforts and collected best available design conventions, procedures, practices and lessons learned in order to provide developers a short compendium of design goals and recommended principles. This targeted review is focused on CDS related to medication prescribing. Published reports suggest that important principles include consistency of design concepts across networked systems, use of appropriate visual representation of clinical data, use of controlled terminology, presenting advice at the time and place of decision making and matching the most appropriate CDS interventions to clinical goals. Specificity and contextual relevance can be increased by periodic review of trigger rules, analysis of performance logs and maintenance of accurate allergy, problem and medication lists in health records in order to help avoid excessive alerting. Developers need to adopt design practices that include user-centered, iterative design and common standards based on human-computer interaction (HCI) research methods rooted in ethnography and cognitive science. Suggestions outlined in this report may help clarify the goals of optimal CDS design but larger national initiatives are needed for systematic application of human factors in health information technology (HIT) development. Appropriate design strategies are essential for developing meaningful decision support systems that meet the grand challenges of high-quality healthcare.

Minimizing Diagnostic Error: The Importance of Follow-up and Feedback

An open-loop system (also called a “nonfeedback controlled” system) is one that makes decisions based solely on preprogrammed criteria and the preexisting model of the system. This approach does not use feedback to calibrate its output or determine if the desired goal is achieved. Because open-loop systems do not observe the output of the processes they are controlling, they cannot engage in learning. They are unable to correct any errors they make or compensate for any disturbances to the process. A commonly cited example of the open-loop system is a lawn sprinkler that goes on automatically at a certain hour each day, regardless of whether it is raining or the grass is already flooded. 1 To an unacceptably large extent, clinical diagnosis is an open-loop system. Typically, clinicians learn about their diagnostic successes or failures in various ad hoc ways (e.g., a knock on the door from a server with a malpractice subpoena; a medical resident learning, upon bumping into a surgical resident in the hospital hallway that a patient he/she cared for has been readmitted; a radiologist accidentally stumbling upon an earlier chest x-ray of a patient with lung cancer and noticing a nodule that had been overlooked). Physicians lack systematic methods for calibrating diagnostic decisions based on feedback from their outcomes. Worse yet, organizations have no way to learn about the thousands of collective diagnostic decisions that are made each day— information that could allow them to both improve overall performance as well as better hear the voices of the patients living with the outcomes. 2

Evaluation of patient centered medical home practice transformation initiatives.

Background:The patient-centered medical home (PCMH) has become a widely cited solution to the deficiencies in primary care delivery in the United States. To achieve the magnitude of change being called for in primary care, quality improvement interventions must focus on whole-system redesign, and not just isolated parts of medical practices. Methods:Investigators participating in 9 different evaluations of Patient Centered Medical Home implementation shared experiences, methodological strategies, and evaluation challenges for evaluating primary care practice redesign. Results:A year-long iterative process of sharing and reflecting on experiences produced consensus on 7 recommendations for future PCMH evaluations: (1) look critically at models being implemented and identify aspects requiring modification; (2) include embedded qualitative and quantitative data collection to detail the implementation process; (3) capture details concerning how different PCMH components interact with one another over time; (4) understand and describe how and why physician and staff roles do, or do not evolve; (5) identify the effectiveness of individual PCMH components and how they are used; (6) capture how primary care practices interface with other entities such as specialists, hospitals, and referral services; and (7) measure resources required for initiating and sustaining innovations. Conclusions:Broad-based longitudinal, mixed-methods designs that provide for shared learning among practice participants, program implementers, and evaluators are necessary to evaluate the novelty and promise of the PCMH model. All PCMH evaluations should as comprehensive as possible, and at a minimum should include a combination of brief observations and targeted qualitative interviews along with quantitative measures.

Drug Formularies: Myths-ln-formation

Drug formularies are privotal tools for delineating and directing prescribing to the “drugs of choice.” Full realization of their potential has been hampered by insufficient comparative data on drug efficacy /safety and local resources for formulary development. However, misconceptions concerning fundamental formulary concepts pose an even more formidable obstacle. This article identifies statements illustrating formulary misconception a) made by physicians attending Pharmacy and Therapeutics Committee meetings during a threeyear period and b) appearing in published sources. The paper highlights basic objectives and operational requirements of an effective formulary, and contrasts this definition with 20 myths and misinformation culled from these two sources. Not only does such misinformation impair formulary development, many critics are so preoccupied with alleged shortcomings that progress in minimizing the real limitations of formularies has been impeded.

Use of health information technology to reduce diagnostic errors

Background Health information technology (HIT) systems have the potential to reduce delayed, missed or incorrect diagnoses. We describe and classify the current state of diagnostic HIT and identify future research directions. Methods A multi-pronged literature search was conducted using PubMed, Web of Science, backwards and forwards reference searches and contributions from domain experts. We included HIT systems evaluated in clinical and experimental settings as well as previous reviews, and excluded radiology computer-aided diagnosis, monitor alerts and alarms, and studies focused on disease staging and prognosis. Articles were organised within a conceptual framework of the diagnostic process and areas requiring further investigation were identified. Results HIT approaches, tools and algorithms were identified and organised into 10 categories related to those assisting: (1) information gathering; (2) information organisation and display; (3) differential diagnosis generation; (4) weighing of diagnoses; (5) generation of diagnostic plan; (6) access to diagnostic reference information; (7) facilitating follow-up; (8) screening for early detection in asymptomatic patients; (9) collaborative diagnosis; and (10) facilitating diagnostic feedback to clinicians. We found many studies characterising potential interventions, but relatively few evaluating the interventions in actual clinical settings and even fewer demonstrating clinical impact. Conclusions Diagnostic HIT research is still in its early stages with few demonstrations of measurable clinical impact. Future efforts need to focus on: (1) improving methods and criteria for measurement of the diagnostic process using electronic data; (2) better usability and interfaces in electronic health records; (3) more meaningful incorporation of evidence-based diagnostic protocols within clinical workflows; and (4) systematic feedback of diagnostic performance.