scholarly journals Standards-Based Clinical Decision Support Platform to Manage Patients Who Meet Guideline-Based Criteria for Genetic Evaluation of Familial Cancer

2020 ◽  
pp. 1-9 ◽  
Author(s):  
Guilherme Del Fiol ◽  
Wendy Kohlmann ◽  
Richard L. Bradshaw ◽  
Charlene R. Weir ◽  
Michael Flynn ◽  
...  
Keyword(s):  
Decision Support ◽  
Pilot Study ◽  
Clinical Decision Support ◽  
Familial Cancer ◽  
Family Health ◽  
Clinical Decision ◽  
Target Population ◽  
Population Based ◽  
Genetic Evaluation ◽  
Pathogenic Variants

PURPOSE The ubiquitous adoption of electronic health records (EHRs) with family health history (FHH) data provides opportunities for tailoring cancer screening strategies to individuals. We aimed to enable a standards-based clinical decision support (CDS) platform for identifying and managing patients who meet guidelines for genetic evaluation of hereditary cancer. METHODS The CDS platform ( www.opencds.org ) was used to implement algorithms based on the 2018 National Comprehensive Cancer Network guidelines for genetic evaluation of hereditary breast/ovarian and colorectal cancer. The platform was designed to be interfaced with different EHR systems via the Health Level Seven International Fast Healthcare Interoperability Resources standard. The platform was integrated with the Epic EHR and evaluated in a pilot study at an academic health care system. RESULTS The CDS platform was executed against a target population of 143,012 patients; 5,245 (3.7%) met criteria for genetic evaluation based on the FHH recorded in the EHR. In a clinical pilot study, genetic counselors attempted to reach out to 71 of the patients. Of those patients, 25 (35%) scheduled an appointment, 10 (14%) declined, 2 (3%) did not need genetic counseling, 7 (10%) said they would consider it in the future, and 27 (38%) were unreachable. To date, 13 (52%) of the scheduled patients completed visits, and 2 (15%) of those were found to have pathogenic variants in cancer predisposition genes. CONCLUSION A standards-based CDS platform integrated with EHR systems is a promising population-based approach to identify patients who are appropriate candidates for genetic evaluation of hereditary cancers.

scholarly journals Direct and Electronic Health Record Access to the Clinical Decision Support for Immunizations in the Minnesota Immunization Information System

2016 ◽  
Vol 8s2 ◽  
pp. BII.S40208
Author(s):  
Sripriya Rajamani ◽  
Aaron Bieringer ◽  
Stephanie Wallerius ◽  
Daniel Jensen ◽  
Tamara Winden ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Clinical Decision ◽  
Population Based ◽  
Direct Access ◽  
Care Providers ◽  
Study Objective ◽  
Vaccination Rates ◽  
Electronic Health

Immunization information systems (IIS) are population-based and confidential computerized systems maintained by public health agencies containing individual data on immunizations from participating health care providers. IIS hold comprehensive vaccination histories given across providers and over time. An important aspect to IIS is the clinical decision support for immunizations (CDSi), consisting of vaccine forecasting algorithms to determine needed immunizations. The study objective was to analyze the CDSi presentation by IIS in Minnesota (Minnesota Immunization Information Connection [MIIC]) through direct access by IIS interface and by access through electronic health records (EHRs) to outline similarities and differences. The immunization data presented were similar across the three systems examined, but with varying ability to integrate data across MIIC and EHR, which impacts immunization data reconciliation. Study findings will lead to better understanding of immunization data display, clinical decision support, and user functionalities with the ultimate goal of promoting IIS CDSi to improve vaccination rates.

scholarly journals Leveraging Electronic Tablets for General Pediatric Care

2015 ◽  
Vol 06 (01) ◽  
pp. 1-15 ◽  
Author(s):  
S. McKee ◽  
T.M. Dugan ◽  
S.M. Downs ◽  
V. Anand
Keyword(s):  
Decision Support ◽  
User Interface ◽  
Pilot Study ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Self Report ◽  
Pediatric Care ◽  
Patient Questionnaire ◽  
Health Improvement ◽  
Completion Rates

SummaryBackground: We have previously shown that a scan-able paper based interface linked to a computerized clinical decision support system (CDSS) can effectively screen patients in pediatric waiting rooms and support the physician using evidence based care guidelines at the time of clinical encounter. However, the use of scan-able paper based interface has many inherent limitations including lacking real time communication with the CDSS and being prone to human and system errors. An electronic tablet based user interface can not only overcome these limitations, but may also support advanced functionality for clinical and research use. However, use of such devices for pediatric care is not well studied in clinical settings.Objective: In this pilot study, we enhance our pediatric CDSS with an electronic tablet based user interface and evaluate it for usability as well as for changes in patient questionnaire completion rates.Methods: Child Health Improvement through Computers Leveraging Electronic Tablets or CHICLET is an electronic tablet based user interface. It is developed to augment the existing scan-able paper interface to our CDSS. For the purposes of this study, we deployed CHICLET in one outpatient pediatric clinic. Usability factors for CHICLET were evaluated via caregiver and staff surveys.Results: When compared to the scan-able paper based interface, we observed an 18% increase or 30% relative increase in question completion rates using CHICLET. This difference was statistically significant. Caregivers and staff survey results were positive for using CHICLET in clinical environment.Conclusions: Electronic tablets are a viable interface for capturing patient self-report in pediatric waiting rooms. We further hypothesize that the use of electronic tablet based interfaces will drive advances in computerized clinical decision support and create opportunities for patient engagement.Citation: Anand V, McKee S, Dugan TM, Downs SM. Leveraging electronic tablets for general pediatric care – a pilot study. Appl Clin Inf 2015; 6: 1–15http://dx.doi.org/10.4338/ACI-2014-09-RA-0071

A pilot study of distributed knowledge management and clinical decision support in the cloud

2013 ◽  
Vol 59 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Brian E. Dixon ◽  
Linas Simonaitis ◽  
Howard S. Goldberg ◽  
Marilyn D. Paterno ◽  
Molly Schaeffer ◽  
...  
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Pilot Study ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Distributed Knowledge

scholarly journals Development of a clinical decision support system for diabetes care: A pilot study

PLoS ONE ◽  
2017 ◽  
Vol 12 (2) ◽  
pp. e0173021 ◽  
Author(s):  
Livvi Li Wei Sim ◽  
Kenneth Hon Kim Ban ◽  
Tin Wee Tan ◽  
Sunil Kumar Sethi ◽  
Tze Ping Loh
Keyword(s):  
Decision Support ◽  
Pilot Study ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Diabetes Care ◽  
Clinical Decision Support System ◽  
Clinical Decision

scholarly journals 1004. Clinical Decision Support System Alerts for HIV Retention in Care – A Pilot Implementation Research Study

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S530-S531
Author(s):  
Michael Leonard ◽  
Rachel P Weber ◽  
Laurence Brunet ◽  
Bernard Davis ◽  
Christopher Polk ◽  
...  
Keyword(s):  
Decision Support ◽  
Pilot Study ◽  
Clinical Decision Support ◽  
Support System ◽  
Implementation Research ◽  
Clinical Decision Support System ◽  
Retention In Care ◽  
Clinical Decision ◽  
Before And After ◽  
Over Time

Abstract Background Clinical decision support system (CDSS) alerts may help retain people living with HIV (PLWH) in care. A system of CDSS alerts utilizing the CHORUS™ portal was developed to identify PLWH at risk of being lost to care. To evaluate feasibility for a larger scale study, a before and after implementation research pilot study was implemented in the OPERA Cohort at three clinic sites in a southeastern US city. Methods Periods without intervention (before) or with CDSS alerts (after) were followed by 3 months of follow up. The study population consisted of PLWH with ≥ 1 electronic health record entry in the 2 years prior to, or during, the before or after period (Fig 1). To support clinicians through a discrete implementation strategy, alerts warning of suboptimal patient attendance were generated daily for the eligible PLWH at each site; providers or other clinic staff could respond to the alerts (Fig 2). Alerts, responses, and visits (i.e., meeting with provider or HIV lab measurement) were characterized. The proportion of PLWH with ≥ 1 visit in the before and after periods were compared at each site by Pearson’s Chi-square. Figure 1. Pilot study timeline Figure 2. CDSS alert criteria and response options Results A total of 12,230 PLWH were eligible (sites A: 11,271; B: 733; C: 1,344 PLWH), with > 75% in both the before and after periods. The ratio of alerts to responses was 11.9 at site A (2,245 alerts to 189 responses in 309 days; Fig 3A), and comparatively lower at sites B (756 alerts to 334 responses in 352 days, ratio=2.2; Fig 3B) and C (1,305 alerts to 896 responses in 246 days, ratio=1.5; Fig 3C). Responses to alerts were sporadic at sites A and B and consistent at site C. After the intervention, the proportion of PLWH with ≥ 1 visit stayed the same at site A (46% in both periods; p=0.47), decreased at site B (91% to 80%; p< 0.01), and increased at site C (72% to 81%; p< 0.01). Figure 3. Alerts and responses over time in (A) Site A, (B) Site B, and (C) Site C Conclusion This pilot study was ecological by design: measures of retention in care were compared over two calendar periods, without accounting for changes in study populations, clinic characteristics, and policies in place over time (which could have impacted clinic attendance). Though engagement with the CDSS was suboptimal at some sites, this implementation pilot study has demonstrated the ability to implement a CDSS aimed at identifying at-risk PLWH, while highlighting areas for improvement in future larger scale studies. Disclosures Joel Wesley Thompson, MHS, PA-C, AAHIVS, DFAAPA, MHS, PA-C, AAHIVS, DFAAPA, Gilead (Shareholder, Speaker’s Bureau)Janssen (Speaker’s Bureau)Theratechnologies (Speaker’s Bureau)ViiV (Speaker’s Bureau) Tammeka Evans, MoP, ViiV Healthcare (Employee)

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