Evaluation, Validation & Implementation of a Computerized Diagnostic Decision Support System in Primary Practice

Background: Medical diagnosis may be the most complex task attempted by humans. Studies estimate that 95% of diagnoses in outpatient care are accurate, implying that the annual rate of inaccurate diagnoses is 12 million in the US alone, with the potential for patient harm in about half. A well-researched differential might reduce inaccurate diagnoses by offering alternatives matching the patient’s symptoms. This study searched the literature for articles evaluating the diagnostic performance of commercially available computerized diagnostic decision support systems. This search led to selecting Isabel Pro, developed by Isabel Healthcare, Ltd. of Haslemere, UK. Evaluation and Validation: A computerized diagnostic decision support system should respond adequately to four questions: What is the “diagnostic retrieval accuracy”? Does it perform as well as clinicians? When provided with the differential, do clinicians improve diagnostic accuracy? Is it easily incorporated into routine practice? The project validated the diagnostic retrieval accuracy of Isabel Pro using 46 cases with a previously confirmed diagnosis. The confirmed diagnosis appeared in Isabel Pro’s differential in 24 cases (52.2%), outperforming even internal medicine faculty (47%). Using those 24 cases and the differentials produced, the author conducted a diagnostic challenge that involved 120 McGovern Medical School residents. The residents produced 406 diagnoses, of which 105 (25.9%) were correct without the differentials, and 37 were correct post-consultation, a 9.1% absolute improvement. In responses, 75.1% of the participants agreed the differentials would be helpful in routine practice, and 64.1% agreed they would consult the differentials if available. Implementation: The project successfully proposed Isabel Pro as a solution to UT practice leadership on September 16, 2021, and incorporated the system into the Epic EHR as a menu line link on November 30, 2021. This system-wide integration also included a QR code for downloading Isabel Pro to a mobile device. Usage of Isabel Pro in the practices of UTPhysicians began on December 8, 2021. Results: The project concluded data collection after 86 days on March 4, 2022, with usage showing a steady increase in the final three weeks. The project produced 73 unique users (37 faculty and 36 residents). The user survey responses showed 83.3% agreeing they would consult the differential generated by Isabel Pro if available at every patient encounter (+19.2% compared to the challenge survey) and 77.8% agreeing that the suggestions would be helpful in routine practice (+2.7% compared to the challenge survey). More than one-third (36.8%) responded that they changed their diagnosis in response to the differential. Limitations: Only usage statistics were analyzed; the system records no reason for the clinician discontinuing a diagnostic session. Only 20 participants responded out of 73 (27.4%), so even though the respondents represented a spread of experience levels, the results may not represent the total number of potential users. The project covered a limited period of 86 days. Conclusions: Diagnostic inaccuracy is a significant patient safety concern. Studies show that computerized diagnostic decision support systems improve diagnostic accuracy, but they are not wide implementation lags despite these findings. This project demonstrated the feasibility of implementing such a well-known system in academic medical practice. The responses to the surveys demonstrate favorable opinions about the system’s perceived usefulness. Active communication and dissemination programs may be essential to improve and sustain use

Clinical decision-making in acute paediatrics : evaluation of the impact of an internet-delivered paediatric decision support system

Imperial Users onl

Use of Decision Tables to Model Assistance Knowledge to Train Medical Residents

En aquesta tesi es presenta un model de coneixement clínic basat en taules de decisió que permet representar les fases de diagnòstic, tractament i pronòstic de diferents malalties. Les taules de decisió que s'obtenen per a cada fase del model han estat utilitzades per representar malalties reals a partir de guies de pràctica clínica. En el cas del diagnòstic s'han representat les vuit causes secundàries més comuns de la hipertensió arterial. En el cas del tractament i pronòstic s'han representat set diferents xocs en emergències. Les taules de decisió que hem obtingut per a cadascuna de les malalties s'han utilitzat com a base per crear dues eines d'entrenament mèdic, dirigides a residents. Totes dues eines s'han provat a l'Hospital Clínic de Barcelona amb diferents grups de residents. Després de les proves s'ha conclòs que les taules de decisió són adequades per a la representació del coneixement mèdic en totes tres fases. A més, les eines d'aprenentatge han estat efectives a l'hora d'ensenyar els procediments mèdics, especialment als residents amb menys experiència prèvia.En esta tesis se presenta un modelo de conocimiento clínico basado en tablas de decisión que permite representar las fases de diagnostico, tratamiento y pronostico de distintas enfermedades. Las tablas de decisión que se obtienen para cada fase del modelo han sido utilizadas para representar enfermedades reales a partir de guías de práctica clínica. En el caso del diagnóstico se han representado las ocho causas secundarias más comunes de la hipertensión arterial. En el caso del tratamiento y pronóstico se han representado siete diferentes shocks en emergencias. Las tablas de decisión que hemos obtenido para cada una de las enfermedades se han usado como base para crear dos herramientas de entrenamiento médico, dirigido a residentes. Ambas herramientas se han probado en el Hospital Clínic de Barcelona con distintos grupos de residentes. Tras las pruebas se ha concluido que las tablas de decisión son adecuadas para la representación del conocimiento medico en las tres fases. Además, las herramientas de aprendizaje han sido efectivas a la hora de enseñar los procedimientos médicos, en especial a los residentes con menos experiencia previa.In this thesis a clinical knowledge model based on decision tables is presented. This model allows us to represent the stages of diagnosis, treatment, and prognosis of different diseases. The decision tables obtained for each phase of the model have been used to represent real diseases from clinical practice guidelines. In the case of diagnosis, we represented eight of the most common secondary causes of hypertension. For the treatment and prognosis we represented seven different emergency shocks. The decision tables obtained for each disease have been used as the basis for two medical training tools aimed to residents. Both tools have been tested in the Hospital Clínic de Barcelona with different groups of residents. After testing, it was concluded that decision tables are suitable for the representation of medical knowledge in all three phases. In addition, the learning tools have been effective in teaching medical procedures, especially for untrained residents

The challenges of using information communication technologies in the healthcare systems in Ethiopia from provider's perspectives

The adoption of eHealth is very slow despite evidences showing its benefits. This research examines the individual, clinical, technical and organizational challenges for eHealth adoption from healthcare provider‟s perspectives. A cross-sectional study design with a quantitative paradigm was used. The study was conducted on 312 doctors and nurses randomly selected from ten hospitals in Addis Ababa, Ethiopia. Most respondents viewed eHealth positively with no significant differences in terms of profession or gender. Computer skill, workload, patient interaction, management support, cost and infrastructure were the main concerns. Privacy and security were not the main concerns. Knowledge of eHealth applications and utilization was low, even for evidence-based medicine and online databases. Specialists and males were better aware of eHealth applications. The study showed that eHealth acceptance was good. Increasing eHealth literacy was recommended as a cost effective means for improving access to updated information to improve the quality of healthcare.Health StudiesM.A. (Public Health (Medical Informatics)

Design and evaluation of a mobile decision support system for screening neurodevelopmental disorders

New Zealand has the highest rate of youth suicide in the developed world. Young persons with ADHD and ASD, amongst other neurodevelopmental disorders, are at greater risk for depression, suicidal ideation, and suicide attempts than their typically developing peers. The early detection and treatment of neurodevelopmental disorders significantly improves the symptomatology and life outcomes of affected individuals. Gaining timely access to specialist mental health clinicians is difficult. This process is made more difficult for children who do not present with overt signs of severe and persistent psychological distress and therefore are not deemed to be ‘high risk’, including the majority of children with ADHD and ASD. Consequently, the diagnosis of children with neurodevelopmental disorders is substantially delayed and/or the responsibility to identify and/or treat falls on clinicians (and non-clinicians) such as general practitioners, general paediatricians, nurses, and educators who may not have the background to have developed complex schemas in this domain. The aim of the present thesis was to develop and test decision support systems that employ different reduced-processing design principles. This was done to determine which principles were effective in assisting individuals who do not possess complex clinical schemas, to diagnose children with neurodevelopmental disorders, in the absence of training and practice with the systems. Several manipulations of decision support system interfaces were developed and tested across different populations of prospective diagnosticians. Across four studies, participants were randomly assigned one of several diagnostic-aids, which differed in their capacity to reduce-processing demands, to assist with clinical diagnosis in a simulated context. Study One: The researcher tested the efficacy of the DSM-5 and two alternate diagnostic-aids that employed one or two reduced-processing design principles that pertained to the arrangement (facilitated the simultaneous acquisition of information) and/or volume (restricted content preconfigured by two clinicians who specialise in neurodevelopmental disorders) of diagnostic information in assisting naïve diagnosticians to diagnose two patients in two separate filmed clinical interviews (both 20-minutes in length). Participants who used the tablet-based diagnostic-aid that employed two (but not one) reduced-processing principles achieved significantly greater diagnostic accuracy scores, with the vast majority of participants in this group correctly (and more efficiently) diagnosing both a seven-year-old with ADHD and a 3-year-old with ASD. There were no significant differences in diagnostic accuracy or response latency between participants who used the DSM-5 and participants who used the diagnostic-aid that employed one reduced-processing principle (restricted, preconfigured content). Study Two: The paradigm from Study One was adopted in Study Two to test the efficacy of the DSM-5 and the diagnostic-aid that employed two reduced-processing design principles in assisting novice diagnosticians (Psychology Master’s students) to diagnose both ASD and ADHD. Participants who used the ‘reduced-processing’ diagnostic-aid achieved significantly greater diagnostic accuracy scores compared to participants who used the DSM-5, with the vast majority of participants in the ‘reduced-processing’ diagnostic-aid group making two correct diagnoses. Study Three: The paradigm from Studies One and Two was adopted in Study Three to examine the extent to which the interactive mechanism of the ‘reduced-processing’ diagnostic-aid to record categorised symptoms of perceived relevance to the patient, influenced diagnostic performance amongst naïve diagnosticians. Three versions of the ‘reduced-processing’ diagnostic-aid, which were identical in content volume and arrangement, were tested: An interface with an interactive mechanism to record categorised symptoms (the ‘Categorisation Interface’ that was used in Studies One and Two), an interface with an interactive mechanism to record uncategorised symptoms (‘Highlighting Interface’), and an interface with no recording mechanism (‘Passive Interface’). Participants who used the ‘Categorisation Interface’ achieved significantly greater diagnostic accuracy scores (and lower response latencies for correct diagnoses of ASD) compared to participants who used the DSM-5, ‘Highlighting Interface’, and ‘Passive Interface’, with the vast majority of participants in the ‘Categorisation Interface’ group making two correct diagnoses. There were no significant differences in diagnostic accuracy or response latency between the DSM-5, ‘Highlighting Interface’, and ‘Passive Interface’ groups. Study Four replicated and extended Study Three. The ASD scenario from the previous studies was adopted in Study Four to examine whether the ‘Categorisation Interface’ needed to be used ‘during + after’ the clinical footage (as it was in Studies 1-3), or if it could also be used ‘after’ the clinical footage to improve diagnostic accuracy. Participants who used the ‘Categorisation Interface’ immediately following the clinical footage were just as accurate (but significantly slower) in diagnosing ASD as participants who used the ‘Categorisation Interface’ both during and immediately following viewing the footage. Conclusion: A decision support system that is structured to facilitate the simultaneous acquisition of a restricted set of preconfigured critical diagnostic features, with the additional capacity to record categorised symptoms of perceived relevance to the patient, was found to assist individuals who do not possess highly developed (or complex) clinical schemas to accurately and efficiently diagnose both ASD and ADHD, in the absence of training and practice with the system. This research has important practical implications for the early detection of neurodevelopmental disorders