A scoping review of clinical decision support tools that generate new knowledge to support decision making in real time

Abstract Objective A growing body of observational data enabled its secondary use to facilitate clinical care for complex cases not covered by the existing evidence. We conducted a scoping review to characterize clinical decision support systems (CDSSs) that generate new knowledge to provide guidance for such cases in real time. Materials and Methods PubMed, Embase, ProQuest, and IEEE Xplore were searched up to May 2020. The abstracts were screened by 2 reviewers. Full texts of the relevant articles were reviewed by the first author and approved by the second reviewer, accompanied by the screening of articles’ references. The details of design, implementation and evaluation of included CDSSs were extracted. Results Our search returned 3427 articles, 53 of which describing 25 CDSSs were selected. We identified 8 expert-based and 17 data-driven tools. Sixteen (64%) tools were developed in the United States, with the others mostly in Europe. Most of the tools (n = 16, 64%) were implemented in 1 site, with only 5 being actively used in clinical practice. Patient or quality outcomes were assessed for 3 (18%) CDSSs, 4 (16%) underwent user acceptance or usage testing and 7 (28%) functional testing. Conclusions We found a number of CDSSs that generate new knowledge, although only 1 addressed confounding and bias. Overall, the tools lacked demonstration of their utility. Improvement in clinical and quality outcomes were shown only for a few CDSSs, while the benefits of the others remain unclear. This review suggests a need for a further testing of such CDSSs and, if appropriate, their dissemination.

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XML Data and Knowledge-Encoding Structure for a Web-Based and Mobile Antenatal Clinical Decision Support System: Development Study

JMIR Formative Research ◽

10.2196/17512 ◽

2020 ◽

Vol 4 (10) ◽

pp. e17512

Author(s):

Ever Augusto Torres Silva ◽

Sebastian Uribe ◽

Jack Smith ◽

Ivan Felipe Luna Gomez ◽

Jose Fernando Florez-Arango

Keyword(s):

Decision Support ◽

Antenatal Care ◽

Clinical Decision Support ◽

Web Application ◽

Clinical Care ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Structure Design ◽

Mobile App ◽

Clinical Workflow

Background Displeasure with the functionality of clinical decision support systems (CDSSs) is considered the primary challenge in CDSS development. A major difficulty in CDSS design is matching the functionality to the desired and actual clinical workflow. Computer-interpretable guidelines (CIGs) are used to formalize medical knowledge in clinical practice guidelines (CPGs) in a computable language. However, existing CIG frameworks require a specific interpreter for each CIG language, hindering the ease of implementation and interoperability. Objective This paper aims to describe a different approach to the representation of clinical knowledge and data. We intended to change the clinician’s perception of a CDSS with sufficient expressivity of the representation while maintaining a small communication and software footprint for both a web application and a mobile app. This approach was originally intended to create a readable and minimal syntax for a web CDSS and future mobile app for antenatal care guidelines with improved human-computer interaction and enhanced usability by aligning the system behavior with clinical workflow. Methods We designed and implemented an architecture design for our CDSS, which uses the model-view-controller (MVC) architecture and a knowledge engine in the MVC architecture based on XML. The knowledge engine design also integrated the requirement of matching clinical care workflow that was desired in the CDSS. For this component of the design task, we used a work ontology analysis of the CPGs for antenatal care in our particular target clinical settings. Results In comparison to other common CIGs used for CDSSs, our XML approach can be used to take advantage of the flexible format of XML to facilitate the electronic sharing of structured data. More importantly, we can take advantage of its flexibility to standardize CIG structure design in a low-level specification language that is ubiquitous, universal, computationally efficient, integrable with web technologies, and human readable. Conclusions Our knowledge representation framework incorporates fundamental elements of other CIGs used in CDSSs in medicine and proved adequate to encode a number of antenatal health care CPGs and their associated clinical workflows. The framework appears general enough to be used with other CPGs in medicine. XML proved to be a language expressive enough to describe planning problems in a computable form and restrictive and expressive enough to implement in a clinical system. It can also be effective for mobile apps, where intermittent communication requires a small footprint and an autonomous app. This approach can be used to incorporate overlapping capabilities of more specialized CIGs in medicine.

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Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

Orphanet Journal of Rare Diseases ◽

10.1186/s13023-020-01536-z ◽

2020 ◽

Vol 15 (1) ◽

Author(s):

Jannik Schaaf ◽

Martin Sedlmayr ◽

Johanna Schaefer ◽

Holger Storf

Keyword(s):

Decision Support ◽

Data Integration ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Clinical Data ◽

Rare Diseases ◽

Scoping Review ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision

Abstract Background Rare Diseases (RDs), which are defined as diseases affecting no more than 5 out of 10,000 people, are often severe, chronic and life-threatening. A main problem is the delay in diagnosing RDs. Clinical decision support systems (CDSSs) for RDs are software systems to support clinicians in the diagnosis of patients with RDs. Due to their clinical importance, we conducted a scoping review to determine which CDSSs are available to support the diagnosis of RDs patients, whether the CDSSs are available to be used by clinicians and which functionalities and data are used to provide decision support. Methods We searched PubMed for CDSSs in RDs published between December 16, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference papers describing a clinical prototype or a routine use of CDSSs were included. For data charting, we used the data items “Objective and background of the publication/project”, “System or project name”, “Functionality”, “Type of clinical data”, “Rare Diseases covered”, “Development status”, “System availability”, “Data entry and integration”, “Last software update” and “Clinical usage”. Results The search identified 636 articles. After title and abstracting screening, as well as assessing the eligibility criteria for full-text screening, 22 articles describing 19 different CDSSs were identified. Three types of CDSSs were classified: “Analysis or comparison of genetic and phenotypic data,” “machine learning” and “information retrieval”. Twelve of nineteen CDSSs use phenotypic and genetic data, followed by clinical data, literature databases and patient questionnaires. Fourteen of nineteen CDSSs are fully developed systems and therefore publicly available. Data can be entered or uploaded manually in six CDSSs, whereas for four CDSSs no information for data integration was available. Only seven CDSSs allow further ways of data integration. thirteen CDSS do not provide information about clinical usage. Conclusions Different CDSS for various purposes are available, yet clinicians have to determine which is best for their patient. To allow a more precise usage, future research has to focus on CDSSs RDs data integration, clinical usage and updating clinical knowledge. It remains interesting which of the CDSSs will be used and maintained in the future.

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AI in Healthcare practice: opportunities and challenges for clinical decision support systems

10.5753/ihc.2019.8372 ◽

2019 ◽

Author(s):

David R. Millen

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Language Processing ◽

Support Systems ◽

Clinical Care ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Lessons Learned ◽

Healthcare Applications

In the past few years there has been great optimism about the potential benefits of incorporating AI (cognitive) capabilities into healthcare products and services. Indeed, progress in Natural Language Processing (NLP) has made electronic health records both more accessible and comprehensible, advances in image processing algorithms has helped to early identify tumors, and large datasets with new discovery services can help with breakthrough insights in life sciences and drug discovery. Importantly, new AI-based solutions are embedded in the sociotechnical systems of clinical care and within complex regulatory environments and globally diverse cultural frameworks. In this talk, I will present several case studies of novel AI – based healthcare applications that have been introduced in recent years and share lessons learned along the way. Particular focus will be on design research challenges for healthcare products, including understanding complex workflows within clinical settings and highly specialized and diverse mental modals, and understanding multiple stakeholders and interdependent participants. Design considerations and emerging opportunities for AI-based clinical decision support systems will also be shared.

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A scoping review of knowledge authoring tools used for developing computerized clinical decision support systems

JAMIA Open ◽

10.1093/jamiaopen/ooab106 ◽

2021 ◽

Vol 4 (4) ◽

Author(s):

Sujith Surendran Nair ◽

Chenyu Li ◽

Ritu Doijad ◽

Paul Nagy ◽

Harold Lehmann ◽

...

Keyword(s):

Decision Support ◽

Clinical Decision Support ◽

Scoping Review ◽

System Integration ◽

Clinical Decision Support Systems ◽

Application Programming Interface ◽

Clinical Decision ◽

Authoring Tools ◽

Clinical Models ◽

Digital Knowledge

Abstract Objective Clinical Knowledge Authoring Tools (CKATs) are integral to the computerized Clinical Decision Support (CDS) development life cycle. CKATs enable authors to generate accurate, complete, and reliable digital knowledge artifacts in a relatively efficient and affordable manner. This scoping review aims to compare knowledge authoring tools and derive the common features of CKATs. Materials and Methods We performed a keyword-based literature search, followed by a snowball search, to identify peer-reviewed publications describing the development or use of CKATs. We used PubMed and Embase search engines to perform the initial search (n = 1579). After removing duplicate articles, nonrelevant manuscripts, and not peer-reviewed publication, we identified 47 eligible studies describing 33 unique CKATs. The reviewed CKATs were further assessed, and salient characteristics were extracted and grouped as common CKAT features. Results Among the identified CKATs, 55% use an open source platform, 70% provide an application programming interface for CDS system integration, and 79% provide features to validate/test the knowledge. The majority of the reviewed CKATs describe the flow of information, offer a graphical user interface for knowledge authors, and provide intellisense coding features (94%, 97%, and 97%, respectively). The composed list of criteria for CKAT included topics such as simulating the clinical setting, validating the knowledge, standardized clinical models and vocabulary, and domain independence. None of the reviewed CKATs met all common criteria. Conclusion Our scoping review highlights the key specifications for a CKAT. The CKAT specification proposed in this review can guide CDS authors in developing more targeted CKATs.

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Assessing the Safety of Custom Web-Based Clinical Decision Support Systems in Electronic Health Records: A Case Study

Applied Clinical Informatics ◽

10.1055/s-0039-1683985 ◽

2019 ◽

Vol 10 (02) ◽

pp. 237-246 ◽

Cited By ~ 2

Author(s):

Jeritt Thayer ◽

Jeffrey Miller ◽

Alexander Fiks ◽

Linda Tague ◽

Robert Grundmeier

Keyword(s):

Decision Support ◽

Clinical Decision Support ◽

Clinical Care ◽

Clinical Decision Support Systems ◽

High Volume ◽

Clinical Decision ◽

Clinical Workflow ◽

System Integrity ◽

Electronic Health

Background With the widespread adoption of vendor-supplied electronic health record (EHR) systems, clinical decision support (CDS) customization efforts beyond those anticipated by the vendor may require the use of technologies external to the EHR such as web services. Pursuing such customizations, however, is not without risk. Validating the expected behavior of a customized CDS system in the high-volume, complex environment of the live EHR is a challenging problem. Objective This article identifies technology failures that impacted clinical care related to web service-based advanced custom CDS systems embedded in the complex sociotechnical context of a production EHR. Methods In an academic health system’s primary care network, we performed an inventory of incidents between January 1, 2008 and December 31, 2016 related to a customized CDS system and performed a targeted review of changes in the CDS source code. Additional feedback on the root cause of individual incidents was obtained through interviews with members of the CDS project teams. Results We identified five CDS malfunctions that impaired clinical workflow. The mechanisms for these failures are mapped to four characteristics of well-behaved applications: (1) system integrity; (2) data integrity; (3) reliability; and (4) scalability. Over the 9-year period, two malfunctions of the customized CDS significantly impaired clinical workflow for a total of 5 hours. Lesser impacts—loss of individual features with straightforward workarounds—arose from three malfunctions, which affected users on 53 days. Discussion Advanced customization of EHRs for the purpose of CDS can present significant risks to clinical workflow. Conclusion This case study highlights that advanced customization of CDS within a commercial EHR may support care for complex patient populations, but ongoing monitoring and support is required to ensure its safe use.

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Integrating Artifact Detection with Clinical Decision Support Systems (Preprint)

10.2196/preprints.23495 ◽

2020 ◽

Author(s):

Shermeen Nizami ◽

Carolyn McGregor ◽

James Robert Green

Keyword(s):

Critical Care ◽

Decision Support ◽

Real Time ◽

Clinical Decision Support ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Signal Quality ◽

Artifact Detection ◽

Physiologic Data

BACKGROUND Clinical decision support systems (CDSS) have the potential to lower patient mortality and morbidity rates. However, signal artifacts present in physiologic data affect the reliability and accuracy of CDSS. Moreover, patient monitors and other medical devices generate false alarms while processing artifactual data. This leads to alarm fatigue due to increased noise levels, staff disruption, and staff desensitization in busy critical care environments. Thereby, adversely affecting the quality of care at the patient bedside. Hence, artifact detection (AD) algorithms play a crucial role in assessing the quality of physiologic data and mitigating the impact of these artifacts. OBJECTIVE Recently, we developed a novel AD framework for integrating AD algorithms with CDSS. The framework was designed with features to support real-time implementation within critical care. In this research, we evaluate the framework and its features in a false alarm reduction study. We develop static framework component models followed by dynamic framework compositions to formulate four CDSS. We evaluate these formulations using neonatal patient data, and validate the six framework features of flexibility, reusability, signal quality indicator standardization, scalability, customizability, and real-time implementation support. METHODS We develop four exemplar static AD components with standardized requirements and provisions interfaces facilitating interoperability of framework components. These AD components are mixed and matched into four different AD compositions to mitigate artifacts. Each AD composition is integrated with a novel static clinical event detection (CED) component to formulate and evaluate dynamic CDSS for arterial oxygen saturation (SpO2) alarms generation. RESULTS With a sensitivity of 80%, the lowest achievable SpO2 false alarm rate is 39%. This demonstrates the utility of the framework in identifying the optimal dynamic composition to serve a given clinical need. CONCLUSIONS The framework features including reusability, signal quality indicator standardization, scalability, and customizability allow for novel CDSS formulations to be evaluated and compared. The optimal solution for a CDSS can then be hard-coded and integrated within clinical workflows for real-time implementation. Flexibility to serve different clinical needs and standardized component interoperability of the framework support the potential for real-time clinical implementation of AD.

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Electronic medical record phenotyping using the anchor and learn framework

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocw011 ◽

2016 ◽

Vol 23 (4) ◽

pp. 731-740 ◽

Cited By ~ 55

Author(s):

Yoni Halpern ◽

Steven Horng ◽

Youngduck Choi ◽

David Sontag

Keyword(s):

Emergency Department ◽

Septic Shock ◽

Nursing Home ◽

Decision Support ◽

Real Time ◽

Medical Record ◽

Electronic Medical Record ◽

Clinical Decision Support ◽

Clinical Decision Support Systems ◽

Clinical Decision

ABSTRACT Background Electronic medical records (EMRs) hold a tremendous amount of information about patients that is relevant to determining the optimal approach to patient care. As medicine becomes increasingly precise, a patient’s electronic medical record phenotype will play an important role in triggering clinical decision support systems that can deliver personalized recommendations in real time. Learning with anchors presents a method of efficiently learning statistically driven phenotypes with minimal manual intervention. Materials and Methods We developed a phenotype library that uses both structured and unstructured data from the EMR to represent patients for real-time clinical decision support. Eight of the phenotypes were evaluated using retrospective EMR data on emergency department patients using a set of prospectively gathered gold standard labels. Results We built a phenotype library with 42 publicly available phenotype definitions. Using information from triage time, the phenotype classifiers have an area under the ROC curve (AUC) of infection 0.89, cancer 0.88, immunosuppressed 0.85, septic shock 0.93, nursing home 0.87, anticoagulated 0.83, cardiac etiology 0.89, and pneumonia 0.90. Using information available at the time of disposition from the emergency department, the AUC values are infection 0.91, cancer 0.95, immunosuppressed 0.90, septic shock 0.97, nursing home 0.91, anticoagulated 0.94, cardiac etiology 0.92, and pneumonia 0.97. Discussion The resulting phenotypes are interpretable and fast to build, and perform comparably to statistically learned phenotypes developed with 5000 manually labeled patients. Conclusion Learning with anchors is an attractive option for building a large public repository of phenotype definitions that can be used for a range of health IT applications, including real-time decision support.

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