What can be done to increase the use of diagnostic decision support systems?

Berner, Eta S.

doi:10.1515/dx-2013-0014

Cited by 9 publications

(9 citation statements)

References 21 publications

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“…Diagnostic decision support systems have not been enthusiastically adopted into routine clinical practice. 19 Possible reasons include lack of high-quality diagnostic data, lack of integration with the EHR and the physician’s workflow, not following human factors principles, support provided too late in the consultation, and lack of physician acceptance or perceived need. 1 , 19 – 21 …”

Section: Discussionmentioning

confidence: 99%

Diagnostic accuracy of GPs when using an early-intervention decision support system: a high-fidelity simulation

et al. 2017

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BackgroundObservational and experimental studies of the diagnostic task have demonstrated the importance of the first hypotheses that come to mind for accurate diagnosis. A prototype decision support system (DSS) designed to support GPs’ first impressions has been integrated with a commercial electronic health record (EHR) system.AimTo evaluate the prototype DSS in a high-fidelity simulation.Design and settingWithin-participant design: 34 GPs consulted with six standardised patients (actors) using their usual EHR. On a different day, GPs used the EHR with the integrated DSS to consult with six other patients, matched for difficulty and counterbalanced.MethodEntering the reason for encounter triggered the DSS, which provided a patient-specific list of potential diagnoses, and supported coding of symptoms during the consultation. At each consultation, GPs recorded their diagnosis and management. At the end, they completed a usability questionnaire. The actors completed a satisfaction questionnaire after each consultation.ResultsThere was an 8–9% absolute improvement in diagnostic accuracy when the DSS was used. This improvement was significant (odds ratio [OR] 1.41, 95% confidence interval [CI] = 1.13 to 1.77, P<0.01). There was no associated increase of investigations ordered or consultation length. GPs coded significantly more data when using the DSS (mean 12.35 with the DSS versus 1.64 without), and were generally satisfied with its usability. Patient satisfaction ratings were the same for consultations with and without the DSS.ConclusionThe DSS prototype was successfully employed in simulated consultations of high fidelity, with no measurable influences on patient satisfaction. The substantially increased data coding can operate as motivation for future DSS adoption.

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Section: Discussionmentioning

confidence: 99%

Diagnostic accuracy of GPs when using an early-intervention decision support system: a high-fidelity simulation

et al. 2017

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“…There are several diagnostic tools such as Isabel® (Isabel Healthcare Inc., USA), Watson® (IBM, Inc. USA), and DxPlain® (Massachusetts General Hospital, Laboratory of Computer Science, Boston, MS) that return a differential diagnosis based on signs, symptoms and demographic information entered by the provider. Although these tools have been shown to improve diagnosis [ 15 – 18 ] they are underutilized for a variety of reasons including the necessity of actively entering data and lack of integration into the EHR [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Early recognition of multiple sclerosis using natural language processing of the electronic health record

Chase

Mitrani

et al. 2017

BMC Med Inform Decis Mak

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BackgroundDiagnostic accuracy might be improved by algorithms that searched patients’ clinical notes in the electronic health record (EHR) for signs and symptoms of diseases such as multiple sclerosis (MS). The focus this study was to determine if patients with MS could be identified from their clinical notes prior to the initial recognition by their healthcare providers.MethodsAn MS-enriched cohort of patients with well-established MS (n = 165) and controls (n = 545), was generated from the adult outpatient clinic. A random sample cohort was generated from randomly selected patients (n = 2289) from the same adult outpatient clinic, some of whom had MS (n = 16). Patients’ notes were extracted from the data warehouse and signs and symptoms mapped to UMLS terms using MedLEE. Approximately 1000 MS-related terms occurred significantly more frequently in MS patients’ notes than controls’. Synonymous terms were manually clustered into 50 buckets and used as classification features. Patients were classified as MS or not using Naïve Bayes classification.ResultsClassification of patients known to have MS using notes of the MS-enriched cohort entered after the initial ICD9[MS] code yielded an ROC AUC, sensitivity, and specificity of 0.90 [0.87-0.93], 0.75[0.66-0.82], and 0.91 [0.87-0.93], respectively. Similar classification accuracy was achieved using the notes from the random sample cohort. Classification of patients not yet known to have MS using notes of the MS-enriched cohort entered before the initial ICD9[MS] documentation identified 40% [23–59%] as having MS. Manual review of the EHR of 45 patients of the random sample cohort classified as having MS but lacking an ICD9[MS] code identified four who might have unrecognized MS.ConclusionsDiagnostic accuracy might be improved by mining patients’ clinical notes for signs and symptoms of specific diseases using NLP. Using this approach, we identified patients with MS early in the course of their disease which could potentially shorten the time to diagnosis. This approach could also be applied to other diseases often missed by primary care providers such as cancer. Whether implementing computerized diagnostic support ultimately shortens the time from earliest symptoms to formal recognition of the disease remains to be seen.

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“…If Ada DX was to be routinely used in clinical practice, the user experience must be improved to reduce active effort. If possible, collected data in Ada DX should be integrated into the electronic health record to avoid double data input and manual work for clinicians, ideally operating in the background and adapting to clinicians' workflows [36].…”

Section: User Input Dependencymentioning

confidence: 99%

Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study

Ronicke

Hirsch²,

Türk³

et al. 2019

Orphanet J Rare Dis

103

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Background: Rare disease diagnosis is often delayed by years. A primary factor for this delay is a lack of knowledge and awareness regarding rare diseases. Probabilistic diagnostic decision support systems (DDSSs) have the potential to accelerate rare disease diagnosis by suggesting differential diagnoses for physicians based on case input and incorporated medical knowledge. We examine the DDSS prototype Ada DX and assess its potential to provide accurate rare disease suggestions early in the course of rare disease cases. Results: Ada DX suggested the correct disease earlier than the time of clinical diagnosis among the top five fit disease suggestions in 53.8% of cases (50 of 93), and as the top fit disease suggestion in 37.6% of cases (35 of 93). The median advantage of correct disease suggestions compared to the time of clinical diagnosis was 3 months or 50% for top five fit and 1 month or 21% for top fit. The correct diagnosis was suggested at the first documented patient visit in 33.3% (top 5 fit), and 16.1% of cases (top fit), respectively. Wilcoxon signed-rank test shows a significant difference between the time to clinical diagnosis and the time to correct disease suggestion for both top five fit and top fit (z-score-6.68, respective-5.71, α=0.05, p-value <0.001). Conclusion: Ada DX provided accurate rare disease suggestions in most rare disease cases. In many cases, Ada DX provided correct rare disease suggestions early in the course of the disease, sometimes at the very beginning of a patient journey. The interpretation of these results indicates that Ada DX has the potential to suggest rare diseases to physicians early in the course of a case. Limitations of this study derive from its retrospective and unblinded design, data input by a single user, and the optimization of the knowledge base during the course of the study. Results pertaining to the system's accuracy should be interpreted cautiously. Whether the use of Ada DX reduces the time to diagnosis in rare diseases in a clinical setting should be validated in prospective studies.

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What can be done to increase the use of diagnostic decision support systems?

Cited by 9 publications

References 21 publications

Diagnostic accuracy of GPs when using an early-intervention decision support system: a high-fidelity simulation

Diagnostic accuracy of GPs when using an early-intervention decision support system: a high-fidelity simulation

Early recognition of multiple sclerosis using natural language processing of the electronic health record

Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study

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