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Clinical Decision Support

Clinical Decision Support (CDS) is an AI-driven tool that empowers researchers and clinicians to navigate the complex landscape of clinical research.
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Most cited protocols related to «Clinical Decision Support»

1
Rapid Assessment Process for Clinical Informatics
The Provider Order Entry Team (POET) at Oregon Health & Science University in Portland, OR was funded to adapt RAP to study clinical decision support (CDS) systems in community hospitals. Subsequently, POET received further funding to use these methods to study CDS systems in outpatient clinics. We have defined computerized provider order entry (CPOE) as a system that allows a provider, such as a doctor or nurse practitioner, to directly enter medical orders via computer. We have defined clinical decision support as “passive and active referential information as well as reminders, alerts, ordersets, and guidelines.”(32 (link))
We generated a fieldwork guide and process for conducting rapid assessments that can be applied to a range of workplace studies in clinical informatics. Please see Appendix A for a sample fieldwork guide. The guide and process have been refined over the course of two years (2007–2009), as we first visited two community hospitals and subsequently visited five ambulatory settings. Starting by using existing examples of protocols and fieldwork guides from public health settings as templates, (23 ;24 ;26 ;27 ) we have been able to successfully and reliably create a fieldwork guide to conduct RAP across a variety of organizations ranging from primary to tertiary care settings.
Our adaptation of RAP for clinical informatics has been informed by guidelines for designing and reporting such evaluations, such as Utarini, Winvist and Pelto’s “11 critical criteria” for conducting RAP(33 ) and the STARE-HI statement of reporting clinical informatics evaluations.(22 (link)) Scrimshaw and Hurtado (23 ) provide numerous examples of data collection guides to be used by RAP team members, including observation guides for studying health-care providers, suggestions for documenting specific health-care processes, and focused interview questions for specific types of health-care personnel. We used these as starting points for creating data collection tools that related to clinical decision support in community hospital and outpatient settings. At the end of each site visit, we discussed changes to our protocol and we met frequently before each site visit in order to tailor our protocol for site-specific conditions.
To date, our fieldwork guide includes the following: 1) a site visit preparation schedule, 2) a pre-visit site profile, 3) a site visit schedule, 4) a fact sheet to be given to subjects, 5) a typical interview guide, 6) a form for field notes, 7) a brief field survey instrument, and 8) an agenda for team debriefings. Data analysis procedures evolved over time to promote reflexivity (awareness of how each team member’s perspective may influence the research process), documentation, and triangulation. Within a few months of a site visit, we conduct our data analysis and write a report of our findings. As we visit multiple sites, we compare themes and findings across sites in order to produce research reports that examine focused topics across various sites. Previously published rapid assessment protocols have emphasized the importance of a fieldwork guide for rigorously documenting evaluation activities, gaining a clear understanding of what team members are expected to do, and ensuring replicability. (23 ;24 ;26 ;27 ) In developing and adapting our procedures over time, we have found that lesson extremely valuable.
McMullen C., Ash J., Sittig D., Bunce A., Guappone K., Dykstra R., Carpenter J., Richardson J, & Wright A. (2010). Rapid Assessment of Clinical Information Systems in the Healthcare Setting: An Efficient Method for Time-Pressed Evaluation. Methods of information in medicine, 50(4), 299-307.
Publication 2010
Acclimatization Awareness Clinical Decision Support Health Personnel Outpatients Physicians Practitioner, Nurse Process Assessment, Health Care Reflex
2
Antibiotic Stewardship Interventions in Primary Care
Suggested alternatives was an EHR-based intervention
most closely resembling traditional clinical decision supports and order sets.
Diagnoses of acute respiratory tract infection triggered a pop-up screen stating
that “Antibiotics are not generally indicated for [this diagnosis].
Please consider the following prescriptions, treatments, and materials to help
your patient,” followed by a list of alternatives (see original protocol
[Appendix F: Example of
Suggested Alternatives Order Set] in Supplement 1), each with
streamlined order entry options for over-the-counter and prescription
medications (eg, decongestants) and letter templates excusing patients from
work. The suggested alternatives intervention drew from the behavioral insight
that prescribers may infer that a suggested (nonantibiotic) alternative ought to
be considered, thus reducing the likelihood that an antibiotic would be
prescribed.19 Accountable justification was also an EHR-based
intervention. An EHR prompt asked each clinician seeking to prescribe an
antibiotic to explicitly justify, in a free text response, his or her treatment
decision. The prompt also informed clinicians that this written justification
would be visible in the patient’s medical record as an “antibiotic
justification note” and that if no justification was entered, the phrase
“no justification given” would appear. Encounters could not be
closed without the clinician’s acknowledgment of the prompt, but
clinicians could cancel the antibiotic order to avoid creating a justification
note, if they chose. The accountable justification alert was triggered for both
antibiotic-inappropriate diagnoses and potentially antibiotic-appropriate acute
respiratory tract infection diagnoses (eg, acute pharyngitis)
The accountable justification intervention was based on prior findings
that accountability improves decision making accuracy and that public
justification engenders reputational concerns.20 (link)–23 (link) To preserve their reputations, clinicians should be
more likely to act in line with injunctive norms24 —that is, what one “ought
to do” as recommended by clinical guidelines.25 Peer comparison was an email-based intervention.
Clinicians were ranked from highest to lowest inappropriate prescribing rate
within each region using EHR data. Clinicians with the lowest inappropriate
prescribing rates (the top-performing decile) were told via monthly email they
were “Top Performers” (see original protocol [Appendix G: Sample Peer Comparison Email
Text] in Supplement
1). The remaining clinicians were told that they were “Not a
Top Performer” in an email that included the number and proportion of
antibiotic prescriptions they wrote for antibiotic-inappropriate acute
respiratory tract infections, compared with the proportion written by top
performers.
Peer comparison was distinct from traditional audit-and-feedback
interventions in its comparison with top-performing peers instead of
average-performing peers and its delivery of positive reinforcement to top
performers—a strategy shown elsewhere to sustain performance.26 –28 (link)
Meeker D., Linder J.A., Fox C.R., Friedberg M.W., Persell S.D., Goldstein N.J., Knight T.K., Hay J.W, & Doctor J.N. (2016). Effect of Behavioral Interventions on Inappropriate Antibiotic Prescribing Among Primary Care Practices: A Randomized Clinical Trial. JAMA, 315(6), 562-570.
Publication 2016
Antibiotics Behavior Therapy Clinical Decision Support Decongestants Diagnosis Dietary Supplements Drugs, Non-Prescription Infection Obstetric Delivery Patients Pharyngitis Positive Reinforcement Prescriptions Respiratory Tract Infections
3
Comprehensive Genomic Profiling Pipeline
The raw sequencing output was transferred from the sequencing instrument to a bioinformatics server running Ubuntu 18.04LTS. A pre-supplied Docker image (the TSO500 pipeline; Illumina, San Diego, CA, USA) was used to generate TMB and microsatellite instability (MSI) calls. The pipeline consists of several steps. Initially, raw bcl files were converted to sample-specific FASTQ files as specified by the sample index. FASTQ files were then aligned against the hg19 reference genome using Isaac 4; local realignment to indels was performed, and paired-end reads were stitched together, followed by variant calling with the somatic sample caller Pisces. Germline variants were filtered using a proprietary database; then the called variants were annotated to identify synonymous and non-synonymous variants. Actual coverage of the panel compared to the reference coverage was computed, and TMB was calculated based on the number of synonymous and non-synonymous mutations detected divided by the size of the panel successfully sequenced.
Small variants were exported from the TSO500 pipeline and annotated using VEP, then converted using vcf2maf and imported into the maftools module of R/Bioconductor.
TMB calls for whole-genome sequenced control data were carried out using the Genomics England v3 pipeline for calling tumour-normal pairs and used to compare to calls from the TSO500 pipeline. In brief, this pipeline utilised Isaac v3 to align sequence data to the hg19 genome, followed by copy-number variant calling using Canvas and structural variant calling using Manta. Copy-number variation (CNV) calls for the TSO500 files were obtained using the Craft copy-number caller set in somatic tumour only mode. Overlaps were computed using bedtools. Structural variant calls for the TSO500 files were obtained using the Manta structural variant caller set in tumour only mode with a custom modification to the C++ code of the Manta structural variant caller to enable detection with less read support and on amplicon sequencing data. Structural variant overlaps were computed using bedtools.
For clinical actionability, raw FASTQ files (CGW, PierianDx, St. Louis, MO, USA) and UMI collapsed vcf files obtained from the TSO500 v1 Docker image (OncoKDM, OncoDNA, Gosselies, Belgium) were uploaded to their respective data portals and run in their standard analysis mode. The Clinical Genomic Workspace (CGW; PierianDx, St Louis, MO, USA) is a secure web-based Health Insurance Portability and Accountability Act- and General Data Protection Regulations-compliant platform for clinical decision support management. Initially developed by one of the very first medical institutes to launch a routine clinical NGS service for cancer and complex inherited diseases, the CGW encompasses a rules engine built on a curated knowledgebase that is updated weekly. Information from over 18 million publications, including Food and Drug Administration (FDA) and European Medicines Agency (EMA) approvals, National Comprehensive Cancer Network (NCCN), Association of Molecular Pathology (AMP) and European Society for Molecular Oncology (ESMO) guidelines and PubMed articles is coupled with public data sources such as population databases, dbSNP, The Cancer Genome Atlas (TCGA), ClinVar and COSMIC in order to annotate and pre-classify variants for interpretation. Uniquely, the CGW utilises the world's largest clinical interpretation-sharing network that provides variant interpretations in the context of the specific disease defined for the patient at time of accessioning. Although no patient data are transferred, network members can view the clinical interpretations supplied to the clinical team of the provider institution (giving the most up-to-date information with true clinical provenance). Actionability calls were downloaded according to standard AMP tiers. The CGW platform is configurable to accept bcl, FASTQ or vcf files and can process all variant types, including TMB and MSI biomarkers, complex variants, CNVs and fusions.
OncoKDM is a secure web-based ISO27001, IS013485 and GDPR-compliant platform for clinical decision support management and clinical report sharing. Initially developed for its proprietary OncoDEEP products that have been on the market since 2013, OncoKDM encompasses a proprietary daily/weekly curated knowledge database of 22,000 genes, 3,886,000 variants, 792 drugs (including FDA and EMA approvals, NCCN, Compermed and ESMO guidelines), 5000 associated clinical trials and 7000 associated publications. Coupled with several public data sources, OncoKDM accurately retrieves biological and clinical information for proper data interpretation and has already been used for 6 years thanks to the sharing platform OncoSHARE, used by 6500 healthcare professionals in 50 countries worldwide.
Pestinger V., Smith M., Sillo T., Findlay J.M., Laes J.F., Martin G., Middleton G., Taniere P, & Beggs A.D. (2020). Use of an Integrated Pan-Cancer Oncology Enrichment Next-Generation Sequencing Assay to Measure Tumour Mutational Burden and Detect Clinically Actionable Variants. Molecular Diagnosis & Therapy, 24(3), 339-349.
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Publication 2020
Biological Markers Biopharmaceuticals Clinical Decision Support Copy Number Polymorphism Cosmic composite resin Diploid Cell Europeans Fishes Genes, vif Genome Germ-Line Mutation Health Care Professionals Hereditary Diseases INDEL Mutation Malignant Neoplasms Microsatellite Instability Missense Mutation Neoplasms Patients Pharmaceutical Preparations Silent Mutation
4
Evaluating Clinical Decision Support for ACS
The study was done at 3 hospitals in North Carolina: Wake Forest Baptist Medical Center (WFBMC), with approximately 114000 ED visits annually; Davie Medical Center (DMC), with approximately 12000 annual ED visits; and Lexington Medical Center (LMC), with approximately 37000 annual ED visits. The target population was adult ED patients (≥21 years old) investigated for possible ACS, but without evidence of ST-segment elevation myocardial infarction (STEMI) on electrocardiography (ECG). Inclusion criteria were the same throughout the pre- and post-implementation periods. Patients with a chief complaint of chest pain and at least one troponin ordered, without evidence of a STEMI on ECG, were accrued. This included patients with known coronary artery disease (prior myocardial infarction, prior coronary revascularization, or known coronary stenosis ≥70%). In addition, patients with other complaints that were concerning for ACS were included if the provider used a study specific EHR flowsheet for possible ACS, which was available in both the Pre- and Post-cohorts.
At WFBMC and DMC, participants were accrued into the pre-implementation cohort (November 2013-October 2014) or the post-implementation cohort (February 2015-January 2016). A wash-in period (November 2014- January 2015) was used to train providers and beta-test an electronic health record (EHR)-based HEART Pathway clinical decision support tool. LMC accrued patients into the pre-implementation (January-July 2015) and post-implementation cohorts (August 2015- January 2016), with a 1-month wash-in period. Patients were accrued into each cohort based on the date of their initial ED visit; later visits for chest pain were considered recurrent care. To prevent accruing more ED repeat users/high utilizers (who often have more co-morbid conditions) into the pre-implementation cohort, patients with an ED visit for possible ACS at each site in the year before the study began (N=523) were excluded from analysis. Patients transferred within the network or visiting multiple sites were classified based on their original ED visit. For transfers, care at the receiving hospital was considered part of their index encounter.
Mahler S.A., Lenoir K.M., Wells B.J., Burke G.L., Duncan P.W., Case L.D., Herrington D.M., Diaz-Garelli J.F., Futrell W.M., Hiestand B.C, & Miller C.D. (2018). Safely Identifying Emergency Department Patients with Acute Chest Pain for Early Discharge: The HEART Pathway Accelerated Diagnostic Protocol. Circulation, 138(22), 2456-2468.
Publication 2018
Adult Chest Pain Clinical Decision Support Coronary Artery Disease Coronary Stenosis Electrocardiography Forests Heart Myocardial Infarction Patients ST Segment Elevation Myocardial Infarction Target Population Troponin
5
UCSDH COVID-19 Incident Response
University of California, San Diego Health (UCSDH) is a large regional academic health system encompassing 2 acute care hospitals, outpatient primary and specialty medical and surgical care, and emergency patient care. UCSDH is also 1 of 5 academic medical centers within a broader 10-campus University of California system. UCSDH utilizes a commercially available, electronic health record (EHR), Epic (Verona, WI), and hosts over 300 affiliate physicians across 10 medical groups on this EHR. San Diego County served as a quarantine site for both Chinese expatriates and cruise ship passengers, and also experienced community spread of COVID earlier than much of the US.19 An Incident Command Center (ICC) was established at UCSDH on February 5, 2020 for 24-hour monitoring and adaptation to rapidly evolving conditions and recommendations on a local, state, federal, and global scale. An assessment of the institutional current state revealed the need to develop a rapid screening process, hospital-based and ambulatory testing, new orders with clinical decision support, reporting/analytics tools, and the enhancement/expansion of current patient-facing technology.
Reeves J.J., Hollandsworth H.M., Torriani F.J., Taplitz R., Abeles S., Tai-Seale M., Millen M., Clay B.J, & Longhurst C.A. (2020). Rapid response to COVID-19: health informatics support for outbreak management in an academic health system. Journal of the American Medical Informatics Association : JAMIA, 27(6), 853-859.
Publication 2020
Acclimatization Chinese Clinical Decision Support Emergencies Enhancement Technologies Operative Surgical Procedures Outpatients Patients Physicians Quarantine Service, Emergency Medical

Most recents protocols related to «Clinical Decision Support»

1
Emergency Medicine Palliative Care Intervention
The study population was full-time EM providers across 33 EDs enrolled in the Primary Palliative Care for Emergency Medicine (PRIM-ER) study. The PRIM-ER study is a cluster-randomized pragmatic trial that assesses the impact of EM provider interventions on healthcare utilization and outcomes among seriously ill older adults that visit the ED.23 The PRIM-ER intervention consists of (1) education in palliative and end-of-life care EM providers and emergency nurses,(2) communication skill training and simulation workshop for EM providers (using the EM Talk training) and emergency nurses (using the End-of-Life Nursing Education Consortium (ELNEC) training), and (3) the integration of a clinical decision support tool to identify and engage seriously ill older adults in SI conversations. We had reported the reach of the ELNEC intervention and emergency nurses’ perceived barriers and solutions to conducting SI conversations in the ED.29 The current study focuses on the reach and effectiveness of EM Talk.
Adeyemi O., Ginsburg A.D., Kaur R., Cuthel A., Zhao N., Siman N., Goldfeld K., Emlet L.L., DiMaggio C., Yamarik R., Bouillon-Minois J.B., Chodosh J, & Grudzen C.R. (2023). Serious Illness Communication Skills Training for Emergency Physicians and Advanced Practice Providers: A Multi-Method Assessment of the Reach and Effectiveness of the Intervention. Research Square.
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Publication Preprint 2023
Aged Clinical Decision Support Emergencies Hospice Care Nurses Palliative Care Speech
2
Remote ECG Monitoring of COVID-19 Patients
The procedures and methods of this study will be made available for replication upon reasonable request directed to the corresponding author. The Institutional Review Board of Universidade Federal de Minas Gerais approved the study under CAAE number 37228120.9.0000.5149.
This is a comparative observational study with prospective data collection. The sample consisted of two arms and one parallel registry, and clinical and electrocardiographic outcomes were assessed remotely. The project was funded by the Brazilian Ministry of Health and conducted by the Telehealth Center of Hospital das Clínicas, Universidade Federal de Minas Gerais (Belo Horizonte, MG, Brazil). Remote data collection occurred in health units connected to the Teleassistance Network of Minas Gerais (Rede de Teleassistência de Minas Gerais–RTMG), and the tele-electrocardiography (ECG) system for COVID-19, in all Brazilian regions.
During the COVID-19 pandemic, RTMG adapted its mobile ECG application to provide clinical decision support for COVID-19 cases in health units, especially in primary care, with demographic and clinical data collection, and ECGs for remote interpretation. It was recommended by health authorities that an ECG be obtained before and following the initiation of drugs for COVID-19. ECGs were captured by commercial equipment linked to specific proprietary software, which allows for getting the ECG signal and clinical data, and transmitted by internet to a central server at the Telehealth Center. The requesting healthcare provider collected baseline history, demographic and clinical data. ECGs were centrally analyzed by a team of experienced cardiologists, utilizing specific semi-automated software with measurement and magnification tools, with visual inspection and subsequent classification by the Minnesota code. Minnesota is the most widely used ECG classification system in the world, developed in the 1950s by Dr. Henry Blackburn, which utilizes a defined set of measurement rules to assign specific numerical codes according to the severity of findings (16 (link), 17 (link)). In the presence of a discrepancy between automated reports and the cardiologist’s interpretation, exams were audited by the study team, composed of three previously trained investigators. All ECGs of patients with suspected COVID-19 in the study period were eligible for this analysis and stored in a specific database.
Nascimento B.R., Paixão G.M., Tonaco L.A., Alves A.C., Peixoto D.C., Ribeiro L.B., Mendes M.S., Gomes P.R., Pires M.C, & Ribeiro A.L. (2023). Clinical and electrocardiographic outcomes evaluated by telemedicine of outpatients with clinical suspicion of COVID-19 treated with chloroquine compounds in Brazil†. Frontiers in Cardiovascular Medicine, 10, 1028398.
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Publication 2023
Arm, Upper Cardiologists Clinical Decision Support COVID 19 DNA Replication Electrocardiogram Electrocardiograph Ethics Committees, Research Health Personnel Patients Pharmaceutical Preparations Primary Health Care Telemedicine
3
Implementation of Patient Blood Management Program
The development and implementation of the PBM program at the Korea University Anam Hospital, Seoul, Republic of Korea, began in January 2018. A task force team was established that revised the PBM program with updated recommendations according to the guidelines,26 which supported restrictive transfusion. Awareness and distribution of the updated guideline recommendations by the PBM program was accomplished via educational programs at workshops carried out within the Anam Hospital in 2018. Furthermore, a Bloodless Medicine Center was established in October 2018, and educational programs were successfully incorporated into various conferences of the medical and surgical departments in 2018 and 2019. After the successful administration of the PBM education program in July 2019, a clinical advisory decision support tool was incorporated into the computerized order communication system. This model required a supplementary process before placing an order for blood transfusion, wherein one of the authorized blood transfusion indications in a pop-up window needed to be selected by the prescribing healthcare provider. Additional lectures regarding other educational programs reflecting the updated model in the computerized order communication system were given at conferences within the Anam Hospital during November and December 2019 to increase awareness of the program. Preoperative diagnosis and treatment of anemia had been actively performed in the emergency room or ward because hip fracture patients required emergency or immediate surgery. The 1,000 mg of intravenous (IV) iron was administered the day before surgery in anemic patients. During surgery, anesthesiologists induced controlled hypotension in patients without cardiovascular disease and maintained normothermia, and performed single-unit RBC transfusion in hemodynamically stable patients. The orthopedic surgeon administered the antifibrinolytic drug, tranexamic acid topically to the surgical site to reduce bleeding. Unnecessary laboratory tests should be reduced to decrease phlebotomy blood loss in perioperatively. Based on the key timeline of the implementation, three periods of the PBM program were defined: pre-PBM (January 2017–December 2017), early-PBM (January 2018–June 2019), and late-PBM (July 2019–December 2020). In addition, two periods of the PBM program were defined for the present study: pre-PBM (January 2017–December 2017) and post-PBM (January 2018–December 2020).
Kim J.H., Shin H.J., You H.S., Park Y., Ahn K.H., Jung J.S., Han S.B, & Park J.H. (2023). Effect of a Patient Blood Management Program on the Appropriateness of Red Blood Cell Transfusion and Clinical Outcomes in Elderly Patients Undergoing Hip Fracture Surgery. Journal of Korean Medical Science, 38(8), e64.
Publication 2023
Aftercare Anemia Anesthesiologist Antifibrinolytic Agents Awareness Biological Models Blood Transfusion Cardiovascular Diseases Clinical Decision Support Conferences Diagnosis Emergencies Health Personnel Hemorrhage Hip Fractures Hypotension, Controlled Iron Operative Surgical Procedures Orthopedic Surgeons Patients Pharmaceutical Preparations Phlebotomy Programmed Learning Surgery, Day TimeLine Tranexamic Acid Workshops
4
Improving CHF Care with Clinical Decision Support
The central features of LEAP were Clinical Decision Support (CDS) tools that were available to clinicians caring for CHF patients. Teams were made aware of the CDS tools by clinical champions and posters in workrooms, and encouraged to use them by a LEAP program manager. CDS tools included an electronic medical record (EMR) order set that prompted teams to order for all CHF patients (a) social work consultation with a standardized screening tool and provision of ride-share vouchers for patients to attend their first post-discharge appointment and a scale for self-weight at home for those who screened positive for limited financial means; (b) enhanced nursing education; (c) nutrition consultation; and (d) electronic referral for scheduling of post-discharge follow-up with a new or established cardiologist. CDS tools also included templates for progress notes including criteria for cardiology consultation, indications for guideline-directed medical therapy (Appendix Figure 3) and guideline-concordant discharge documentation. Additionally, patients received a post-discharge phone call from a clinical pharmacist to screen for any medication-related safety concerns.
Osuagwu C., Khinkar R.M., Zheng A., Wien M., Decopain J., Desai S., McElrath E., Hinchey E., Mueller S.K., Schnipper J.L., Boxer R, & Shannon E.M. (2023). A Public Health Critical Race Praxis Informed Congestive Heart Failure Quality Improvement Initiative on Inpatient General Medicine. Journal of General Internal Medicine, 38(10), 2236-2244.
Publication 2023
Cardiologists Cardiovascular System Clinical Decision Support Clinical Pharmacists Education, Nursing Patient Discharge Patients Pharmaceutical Preparations Safety Therapeutics
5
Personalized ADLIFE Healthcare Intervention
The ADLIFE intervention consists of the deployment and use of the ADLIFE toolbox by patients, informal caregivers, and healthcare professionals in the afore-mentioned pilot settings.
The ADLIFE toolbox involves two interconnected platforms. Patients will use the Patient Empowerment Platform (PEP), and healthcare professionals will be assisted by clinical decision support services within the Personalized Care Plan Management Platform (PCPMP). Patients participating in ADLIFE will have a personalized care plan, created in PCPMP, which will be developed and managed together with their healthcare professionals. PCPMP will be used in integration with the clinical sites’ ICT systems to create patient care plans based on each patient’s baseline and most recent clinical data, following clinical evidence. PEP will facilitate the patient’s independence and self-management by presenting their personalized goals, activities, and educational materials, collecting their observations and questionnaires, and providing real-time interventions tailored to the patient’s lifestyle.
The main task of patients and their informal caregivers will be to use the PEP as part of their healthcare management process together with their healthcare professionals. The ADLIFE intervention will consider the health-related outcomes relevant for the patient in actual health service planning and evaluation. By identifying the outcome that will be responsive to each measure, professionals and patients will have the chance of reviewing the health-related outcomes, and of jointly choosing the activity, objective or goal that boosts the desired one. The health-related outcomes will be reflected as labels that bind every activity, goal, and/or indicator included in a care plan. The labelling mechanism has been co-created with healthcare professionals and automated to enable health-related outcome tracking over time and over a wide spectrum of patients.
The control group follows the SoC according to the pilot site organizations’ criteria. Since pilot sites belong to different health care systems, information on SoC was derived from semi-structured interviews with three stakeholder groups, 5–7 persons in each group [21 ].
García-Lorenzo B., Gorostiza A., González N., Larrañaga I., Mateo-Abad M., Ortega-Gil A., Bloemeke J., Groene O., Vergara I., Mar J., Lim Choi Keung S.N., Arvanitis T.N., Kaye R., Dahary Halevy E., Nahir B., Arndt F., Dichmann Sorknæs A., Juul N.K., Lilja M., Sherman M.H., Laleci Erturkmen G.B., Yuksel M., Robbins T., Kyrou I., Randeva H., Maguire R., McCann L., Miller M., Moore M., Connaghan J., Fullaondo A., Verdoy D, & de Manuel Keenoy E. (2023). Assessment of the Effectiveness, Socio-Economic Impact and Implementation of a Digital Solution for Patients with Advanced Chronic Diseases: The ADLIFE Study Protocol. International Journal of Environmental Research and Public Health, 20(4), 3152.
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Publication 2023
Clinical Decision Support Health Personnel Health Services Administration Informal Caregivers Patient Care Planning Patients Self-Management

Top products related to «Clinical Decision Support»

1
Epic 2016 by Epic Systems
Sourced in United States
The Epic 2016 is a comprehensive laboratory equipment product designed for use in various scientific and medical research settings. It is capable of performing a wide range of analytical and diagnostic tasks, though its core function is not specified in detail in order to maintain an unbiased and factual approach.
2
Ehr system by Epic Systems
The EHR system is a comprehensive electronic health record platform that allows healthcare providers to manage patient data, including medical history, treatments, and test results. The system integrates various functionalities to streamline clinical workflows and facilitate efficient patient care.
3
Spss statistics version 26 by IBM
Sourced in United States, Japan, United Kingdom, Austria, Germany, Belgium, Poland, Italy
SPSS Statistics version 26 is a comprehensive statistical software package designed for data analysis and management. It provides a wide range of statistical techniques, including descriptive statistics, regression analysis, and data mining. The software is commonly used in various industries and research fields to gain insights from data.
4
Virus minikits 2 by Qiagen
The Virus Minikits 2.0 are a series of laboratory equipment designed for the extraction and purification of viral nucleic acids. The kits provide a streamlined workflow for the isolation of viral RNA or DNA from various sample types, including cell cultures, clinical specimens, and environmental samples. The Virus Minikits 2.0 utilize optimized protocols and reagents to ensure efficient and reliable results.
5
Nvivo 10 by Lumivero
Sourced in Australia, United States, United Kingdom
NVivo 10 is a software package designed for qualitative and mixed-methods research. It provides tools for organizing, analyzing, and visualizing non-numerical data, such as interview transcripts, documents, and multimedia files.
6
R version 3 by R Project for Statistical Computing
Sourced in United States, Austria, Japan, Belgium, New Zealand, United Kingdom, Germany, Denmark, Australia, France
R version 3.6.1 is a statistical computing and graphics software package. It is an open-source implementation of the S programming language and environment. R version 3.6.1 provides a wide range of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and more.
7
Tni ultra assay by Siemens
Sourced in United States
The TnI-Ultra assay is a quantitative in vitro diagnostic test used for the measurement of cardiac troponin I (cTnI) levels in human serum or plasma samples. The assay is designed to aid in the diagnosis of myocardial infarction and other acute coronary syndromes. The TnI-Ultra assay provides a reliable and accurate measurement of cTnI concentrations, which can be used by healthcare professionals to evaluate cardiac health and guide clinical decision-making.
8
Stata mp 17 by StataCorp
Sourced in United States, United Kingdom
Stata/MP 17.0 is a statistical software package designed for data analysis, modeling, and visualization. It provides a powerful and flexible platform for managing, analyzing, and interpreting complex data sets. Stata/MP 17.0 offers enhanced performance and parallelization capabilities, enabling faster computations and more efficient data processing.

More about "Clinical Decision Support"

Clinical Decision Support (CDS) is an innovative, AI-powered tool that revolutionizes the way researchers and clinicians navigate the complex landscape of clinical research.
By seamlessly integrating data from literature, preprints, and patents, CDS leverages advanced AI algorithms to provide users with a smarter, more efficient approach to identifying the best protocols and products for their specific needs.
CDS is designed to optimize research workflows, empowering users to make more informed decisions and ultimately improve patient outcomes.
This cutting-edge technology can be seamlessly integrated with Electronic Health Record (EHR) systems, such as Epic 2016, providing a consolidated view of patient data and research protocols.
Beyond literature and patents, CDS can also leverage data from other sources, including SPSS Statistics version 26, Virus Minikits 2.0, NVivo 10, R version 3.6.1, and Stata/MP 17.0, to deliver comprehensive, data-driven insights.
By harnessing the power of AI-driven comparisons, CDS helps users identify the most relevant and effective protocols, reducing the time and effort required to navigate the vast array of clinical research options.
The use of CDS can lead to significant improvements in research efficiency and decision-making.
For example, the TnI-Ultra assay, a highly sensitive cardiac biomarker test, can be quickly and easily identified using CDS, enabling clinicians to make more informed treatment decisions and improve patient outcomes.
In summary, Clinical Decision Support (CDS) is a transformative tool that empowers researchers and clinicians to navigate the complex world of clinical research with ease and efficiency.
By seamlessly integrating data from multiple sources and leveraging advanced AI algorithms, CDS helps users identify the best protocols and products, optimizing research workflows and driving innovation forward.