Improving the Delivery of Healthcare through Clinical Diagnostic Insights: A Valuation of Laboratory Medicine through “Clinical Lab 2.0”

2018 ◽  
Vol 3 (3) ◽  
pp. 487-497 ◽  
Author(s):  
Kathleen Swanson ◽  
Monique R Dodd ◽  
Richard VanNess ◽  
Michael Crossey
Keyword(s):  
Decision Support ◽  
Data Analysis ◽  
Population Health ◽  
Clinical Decision Support ◽  
Data Analytics ◽  
Health Management ◽  
Clinical Decision ◽  
Laboratory Medicine ◽  
Population Health Management ◽  
Clinical Diagnostic

Abstract Background As healthcare payment and reimbursement begin to shift from a fee-for-service to a value-based model, ancillary providers including laboratories must incorporate this into their business strategy. Laboratory medicine, while continuing to support a transactional business model, should expand efforts to include translational data analytics, proving its clinical and economic valuation. Current literature in this area is limited. Content This article is a summary of how laboratory medicine can support value-based healthcare. Population health management is emerging as a method to support value-based healthcare by aggregating patient information, providing data analysis, and contributing to clinical decision support. Key issues to consider with a laboratory-developed population health management model are discussed, including changing reimbursement models, the use of multidisciplinary committees, the role of specialists in data analytics and programming, and barriers to implementation. Examples of data considerations and value are given. Summary Laboratory medicine is able to provide meaningful clinical diagnostic insights for population health initiatives that result in improved short- and long-term patient outcomes and drive cost-effective care. Opportunities include data analysis with longitudinal laboratory data, identification of patient-specific targeted interventions, and development of clinical decision support tools. Laboratories will need to leverage the skills and knowledge of their multidisciplinary staff, along with their extensive patient data sets, through innovative analytics to meet these objectives.

Characterizing Workflow to Inform Clinical Decision Support Systems in Nursing Homes

2018 ◽  
Vol 59 (6) ◽  
pp. 1024-1033 ◽  
Author(s):  
Mustafa Ozkaynak ◽  
Blaine Reeder ◽  
Cynthia Drake ◽  
Peter Ferrarone ◽  
Barbara Trautner ◽  
...  
Keyword(s):  
Decision Support ◽  
Data Analysis ◽  
Nursing Homes ◽  
Clinical Decision Support ◽  
Care Delivery ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Staff Members ◽  
Design And Implementation ◽  
Multiple Levels

Abstract Background and Objectives Clinical decision support systems (CDSS) hold promise to influence clinician behavior at the point of care in nursing homes (NHs) and improving care delivery. However, the success of these interventions depends on their fit with workflow. The purpose of this study was to characterize workflow in NHs and identify implications of workflow for the design and implementation of CDSS in NHs. Research Design and Methods We conducted a descriptive study at 2 NHs in a metropolitan area of the Mountain West Region of the United States. We characterized clinical workflow in NHs, conducting 18 observation sessions and interviewing 15 staff members. A multilevel work model guided our data collection and framework method guided data analysis. Results The qualitative analysis revealed specific aspects of multilevel workflow in NHs: (a) individual, (b) work group/unit, (c) organization, and (d) industry levels. Data analysis also revealed several additional themes regarding workflow in NHs: centrality of ongoing relationships of staff members with the residents to care delivery in NHs, resident-centeredness of care, absence of memory aids, and impact of staff members’ preferences on work activities. We also identified workflow-related differences between the two settings. Discussion and Implications Results of this study provide a rich understanding of the characteristics of workflow in NHs at multiple levels. The design of CDSS in NHs should be informed by factors at multiple levels as well as the emergent processes and contextual factors. This understanding can allow for incorporating workflow considerations into CDSS design and implementation.

scholarly journals A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6209
Author(s):  
Andrei Velichko
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Support ◽  
Data Analysis ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Classification Accuracy ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Data

Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these methods on low-power microcontrollers with small memory size calls for the development of new effective algorithms for neural networks. This study presents a new method for analyzing medical data based on the LogNNet neural network, which uses chaotic mappings to transform input information. The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators. The efficiency of LogNNet in assessing perinatal risk is illustrated on cardiotocogram data obtained from the UC Irvine machine learning repository. The classification accuracy reaches ~91% with the~3–10 kB of RAM used on the Arduino microcontroller. Using the LogNNet network trained on a publicly available database of the Israeli Ministry of Health, a service concept for COVID-19 express testing is provided. A classification accuracy of ~95% is achieved, and~0.6 kB of RAM is used. In all examples, the model is tested using standard classification quality metrics: precision, recall, and F1-measure. The LogNNet architecture allows the implementation of artificial intelligence on medical peripherals of the Internet of Things with low RAM resources and can be used in clinical decision support systems.

scholarly journals SNOMED CT Concept Hierarchies for Sharing Definitions of Clinical Conditions Using Electronic Health Record Data

2018 ◽  
Vol 09 (03) ◽  
pp. 667-682 ◽  
Author(s):  
Duwayne Willett ◽  
Vaishnavi Kannan ◽  
Ling Chu ◽  
Joel Buchanan ◽  
Ferdinand Velasco ◽  
...  
Keyword(s):  
Decision Support ◽  
Population Health ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Health Record ◽  
Snomed Ct ◽  
Patient Registries ◽  
Value Sets ◽  
Clinical Conditions ◽  
Concept Hierarchies

Background Defining clinical conditions from electronic health record (EHR) data underpins population health activities, clinical decision support, and analytics. In an EHR, defining a condition commonly employs a diagnosis value set or “grouper.” For constructing value sets, Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT) offers high clinical fidelity, a hierarchical ontology, and wide implementation in EHRs as the standard interoperability vocabulary for problems. Objective This article demonstrates a practical approach to defining conditions with combinations of SNOMED CT concept hierarchies, and evaluates sharing of definitions for clinical and analytic uses. Methods We constructed diagnosis value sets for EHR patient registries using SNOMED CT concept hierarchies combined with Boolean logic, and shared them for clinical decision support, reporting, and analytic purposes. Results A total of 125 condition-defining “standard” SNOMED CT diagnosis value sets were created within our EHR. The median number of SNOMED CT concept hierarchies needed was only 2 (25th–75th percentiles: 1–5). Each value set, when compiled as an EHR diagnosis grouper, was associated with a median of 22 International Classification of Diseases (ICD)-9 and ICD-10 codes (25th–75th percentiles: 8–85) and yielded a median of 155 clinical terms available for selection by clinicians in the EHR (25th–75th percentiles: 63–976). Sharing of standard groupers for population health, clinical decision support, and analytic uses was high, including 57 patient registries (with 362 uses of standard groupers), 132 clinical decision support records, 190 rules, 124 EHR reports, 125 diagnosis dimension slicers for self-service analytics, and 111 clinical quality measure calculations. Identical SNOMED CT definitions were created in an EHR-agnostic tool enabling application across disparate organizations and EHRs. Conclusion SNOMED CT-based diagnosis value sets are simple to develop, concise, understandable to clinicians, useful in the EHR and for analytics, and shareable. Developing curated SNOMED CT hierarchy-based condition definitions for public use could accelerate cross-organizational population health efforts, “smarter” EHR feature configuration, and clinical–translational research employing EHR-derived data.

scholarly journals The Effect of Laboratory Test–Based Clinical Decision Support Tools on Medication Errors and Adverse Drug Events: A Laboratory Medicine Best Practices Systematic Review

2019 ◽  
Vol 3 (6) ◽  
pp. 1035-1048
Author(s):  
Nedra S Whitehead ◽  
Laurina Williams ◽  
Sreelatha Meleth ◽  
Sara Kennedy ◽  
Nneka Ubaka-Blackmoore ◽  
...  
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Best Practice ◽  
Adverse Drug Events ◽  
Clinical Decision ◽  
Laboratory Medicine ◽  
Bibliographic Databases ◽  
Laboratory Monitoring ◽  
Meta Analyses

Abstract Background Laboratory and medication data in electronic health records create opportunities for clinical decision support (CDS) tools to improve medication dosing, laboratory monitoring, and detection of side effects. This systematic review evaluates the effectiveness of such tools in preventing medication-related harm. Methods We followed the Laboratory Medicine Best Practice (LMBP) initiative's A-6 methodology. Searches of 6 bibliographic databases retrieved 8508 abstracts. Fifteen articles examined the effect of CDS tools on (a) appropriate dose or medication (n = 5), (b) laboratory monitoring (n = 4), (c) compliance with guidelines (n = 2), and (d) adverse drug events (n = 5). We conducted meta-analyses by using random-effects modeling. Results We found moderate and consistent evidence that CDS tools applied at medication ordering or dispensing can increase prescriptions of appropriate medications or dosages [6 results, pooled risk ratio (RR), 1.48; 95% CI, 1.27–1.74]. CDS tools also improve receipt of recommended laboratory monitoring and appropriate treatment in response to abnormal test results (6 results, pooled RR, 1.40; 95% CI, 1.05–1.87). The evidence that CDS tools reduced adverse drug events was inconsistent (5 results, pooled RR, 0.69; 95% CI, 0.46–1.03). Conclusions The findings support the practice of healthcare systems with the technological capability incorporating test-based CDS tools into their computerized physician ordering systems to (a) identify and flag prescription orders of inappropriate dose or medications at the time of ordering or dispensing and (b) alert providers to missing laboratory tests for medication monitoring or results that warrant a change in treatment. More research is needed to determine the ability of these tools to prevent adverse drug events.

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