Performance of Four Computer-Based Diagnostic Systems

Berner, Eta S.; Webster, George D.; Shugerman, A. A.; Jackson, James R.; Algina, James; Baker, Alfred L.; Ball, Eugene V.; Cobbs, Charles; Dennis, Vincent W.; Frenkel, Eugene P.; Hudson, Leonard D.; Mancall, Elliott L.; Rackley, Charles E.; Taunton, O. David

doi:10.1056/nejm199406233302506

Cited by 270 publications

(171 citation statements)

References 33 publications

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“…Adult critical care Diagnostic DXplain [28] Mortality and length of stay prediction [9,29] Alert and reminder Ventilator induced lung injury [84] Blood pressure variability while on vasopressors [32] Adverse drug reactions [19] Drug induced thrombocytopenia [30] Epidural hematoma with neuraxial anesthesia [31] Protocol/procedure Acute respiratory distress syndrome [33][34][35] Sepsis [21] VTE prophylaxis and events in trauma patients [37] VTE prophylaxis [38,39] Tidal volume during mechanical ventilation [36] Management…”

Section: Type Of Support Tool Example or Subjectmentioning

confidence: 99%

“…Examples of CDS systems in adult critical care are provided in Table 1. Diagnostic support tools are available to assist in disease identification and also for using symptoms and patient condition on admission to predict outcome [9,28,29] . Ranson's criteria and various APACHE models are examples that have been validated using real time data to predict mortality risk in critically ill patients [29] .…”

Section: Type Of Support Tool Example or Subjectmentioning

confidence: 99%

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Computerized decision support in adult and pediatric critical care

Williams¹,

Bratton²

2013

WJCCM

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Computerized decision support (CDS) is the most advanced form of clinical decision support available and has evolved with innovative technologies to provide meaningful assistance to medical professionals. Critical care clinicians are in unique environments where vast amounts of data are collected on individual patients, and where expedient and accurate decisions are paramount to the delivery of quality healthcare. Many CDS tools are in use today among adult and pediatric intensive care units as diagnostic aides, safety alerts, computerized protocols, and automated recommendations for management. Some CDS use have significantly decreased adverse events and improved costs when carefully implemented and properly operated. CDS tools integrated into electronic health records are also valuable to researchers providing rapid identification of eligible patients, streamlining data-gathering and analysis, and providing cohorts for study of rare and chronic diseases through data-warehousing. Although the need for human judgment in the daily care of critically ill patients has limited the study and realization of meaningful improvements in overall patient outcomes, CDS tools continue to evolve and integrate into the daily workflow of clinicians, and will likely provide advancements over time. Through novel technologies, CDS tools have vast potential for progression and will significantly impact the field of critical care and clinical research in the future.

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Section: Type Of Support Tool Example or Subjectmentioning

confidence: 99%

Section: Type Of Support Tool Example or Subjectmentioning

confidence: 99%

Computerized decision support in adult and pediatric critical care

Williams¹,

Bratton²

2013

WJCCM

View full text Add to dashboard Cite

show abstract

“…A wide variety of techniques has been used to do this, predominantly stemming from Bayesian probability, decision analysis, and artificial intelligence. After evaluations showed a poor uptake of expert systems (Miller & Masarie, 1990) and modest benefit of diagnostic systems (Berner et al, 1994), emphasis has shifted towards the implementation of clinical practice guidelines and protocols. These systems generally build on computer-interpretable models of the guidelines or protocols in question, and are described in Section 4.2.…”

Section: Computerized Decision Supportmentioning

confidence: 99%

Guideline-Based Decision Support Systems for Prevention and Management of Chronic Diseases

Peek¹

2011

Efficient Decision Support Systems - Practice and Challenges in Biomedical Related Domain

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“…9 In that study, four DDX generators were evaluated, two of which are no longer on the market, and only one of which met the inclusion criteria of the current study. Berner and colleagues concluded that on average the tools had a relatively mediocre sensitivity of approximately 50-70% for the correct diagnosis, and a companion editorial by Kassirer gave the tools a grade of C. 10 The current study by Bond and colleagues has many strengths.…”

mentioning

confidence: 99%

A Follow-Up Report Card on Computer-Assisted Diagnosis—the Grade: C+

Umscheid

Hanson

2011

J GEN INTERN MED

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D iagnostic errors result in an estimated 40,000 to 80,000 US hospital deaths annually 1 ; they are the most common type of medical error cited in malpractice claims and result in the largest payouts 2 ; they lead to more patient readmissions than other types of errors 3 ; and autopsy studies confirm the ubiquity of missed diagnoses. 4 Although not all diagnostic errors result in harm, many do. 5 Typically these mistakes represent either cognitive errors, such as those resulting from a lack of knowledge, or communication errors, such as those resulting from poor handoffs and follow-up. 6,7 Differential diagnosis (DDX) generators have the potential to assist in the former, while other types of clinical decision support can address the latter.In this issue of JGIM, Bond and colleagues examine the features and performance of four DDX generators. 8 They identified these tools through a systematic search, compared each tool against a set of consensus criteria, and tested each tool using diagnostic conundrums from the New England Journal of Medicine (NEJM) and the Medical Knowledge Self Assessment Program (MKSAP) of the American College of Physicians. The best tools demonstrated sensitivities as high as 65% and a number of features which advanced their usability, including direct links to evidence resources, the ability to input multiple variables including negative findings, the potential to be integrated with electronic health records (EHRs) to obviate the need for manual data entry, and mobile access.Unfortunately, the performance results were not much better than programs examined in a similar study published in the NEJM nearly 20 years ago by Berner and colleagues. 9 In that study, four DDX generators were evaluated, two of which are no longer on the market, and only one of which met the inclusion criteria of the current study. Berner and colleagues concluded that on average the tools had a relatively mediocre sensitivity of approximately 50-70% for the correct diagnosis, and a companion editorial by Kassirer gave the tools a grade of C. 10 The current study by Bond and colleagues has many strengths. First, their search for DDX generators was systematic and comprehensive, including searches of the internet and medical literature databases, as well as expert consultation. The appendix includes a list of programs which were identified but did not meet inclusion criteria. The only major tool type that was not considered for inclusion in their study was the internet search engine (such as the generic Google or the medically targeted WebMD). Evidence suggests that these search tools can lead to the correct diagnosis in over 50% of cases, similar to the sensitivities of the DDX generators in this study. 11 Thus, head-to-head comparisons of DDX generators with internet search engines will be critical to demonstrating the incremental value of the proprietary, specialized software. Of note, at least one of the tools examined in Bond's study offered structured Google searching of pre-selected medical websites.A second stre...

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Performance of Four Computer-Based Diagnostic Systems

Abstract: The results provide a profile of the strengths and limitations of these computer programs. The programs should be used by physicians who can identify and use the relevant information and ignore the irrelevant information that can be produced.

Cited by 270 publications

References 33 publications

Computerized decision support in adult and pediatric critical care

Computerized decision support in adult and pediatric critical care

Guideline-Based Decision Support Systems for Prevention and Management of Chronic Diseases

A Follow-Up Report Card on Computer-Assisted Diagnosis—the Grade: C+

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