scholarly journals Improving patient prioritization during hospital-homecare transition: A protocol of a mixed-methods study of a clinical decision support tool implementation (Preprint)

2020 ◽  
Author(s):  
Maryam Zolnoori ◽  
Margaret McDonald ◽  
Kenrick Cato ◽  
Paulina Sockolow ◽  
Nicole Onorato ◽  
...  
Keyword(s):  
Decision Support ◽  
Mixed Methods ◽  
Patient Outcomes ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
The United States ◽  
Support Tool ◽  
Propensity Score Matching Analysis ◽  
Healthcare Needs ◽  
Nursing Visit

BACKGROUND Homecare settings across the United States provide care to more than 5 million patients every year. About one in five homecare patients are rehospitalized during the homecare episode, with up to two-thirds of these rehospitalizations occurring within the first two weeks of services. Timely alloca-tion of homecare services might prevent a significant portion of these rehospitalizations. The first homecare nursing visit is one of the most critical steps of the homecare episode. This visit includes an assessment of the patient's capacity for self-care, medication reconciliation, an examination of the home environment, and a discussion regarding whether a caregiver is present. Hence, appro-priate timing of the first visit is crucial, especially for patients with urgent healthcare needs. However, nurses often have limited and inaccurate information about incoming patients and patient priority decisions vary significantly between nurses. We developed an innovative decision support tool called “Priority for the First Nursing Visit Tool” (PREVENT) to assist nurses in prioritizing patients in need of immediate first homecare nursing visits. OBJECTIVE To evaluate the effectiveness of the PREVENT tool on process and patient outcomes; and to exam-ine aspects of PREVENT’s reach, adoption, and implementation. METHODS Employing a pre post design, and survival analysis and logistic regression with propensity score matching analysis we will test the following hypotheses: Compared to not using the tool in the pre-intervention phase, when homecare clinicians use the PREVENT tool, high risk patients in the inter-vention phase will: a) receive more timely first homecare visits and b) have decreased incidence of rehospitalization and have decreased emergency department (ED) use within 60 days. Reach, adoption, and implementationwill be assessed using mixed methods including homecare admis-sion staff interviews, think-aloud observations, and analysis of staffing and other relevant data. RESULTS The study research protocol was approved by the institutional review board in October 2019. PRE-VENT is currently being integrated into the electronic health records at the participating study sites. Data collection is planned to start in early 2021. CONCLUSIONS Mixed methods will enable us to gain in-depth understanding of complex socio-technological as-pects of the hospital to homecare transition. The results have the potential to (1) influence the standardization and individualization of nurse decision making thru the use of cutting-edge technol-ogy and (2) improve patient outcomes in the understudied homecare setting. CLINICALTRIAL ClinicalTrials.gov Identifier: NCT04136951, https://clinicaltrials.gov/ct2/show/NCT04136951?term=Maxim+Topaz&id=NCT04136951&draw=2&rank=1

Rationale and design of an evidence-based tool to guide preoperative evaluation and management

2020 ◽  
Vol 31 (1-2) ◽  
pp. 24-30
Author(s):  
Alex R Campbell ◽  
David P Ingham ◽  
Michele F Shepherd ◽  
Joshua J Mueller ◽  
Timothy D Henry ◽  
...  
Keyword(s):  
Decision Support ◽  
Healthcare System ◽  
Preoperative Evaluation ◽  
Preoperative Assessment ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
The United States ◽  
Evidence Based ◽  
Preoperative Testing ◽  
Support Tool

Background In the United States, over-testing and over-treatment are recognised causes of excess cost and patient harm. Healthcare value, defined as health outcomes achieved relative to the costs of care, has become a focus to improve the quality and affordability of healthcare. Aim To describe the rationale for, and development of a standardised clinical preoperative decision-support tool. Program description: An evidence-based, preoperative clinical decision tool was developed to guide preoperative testing and management of high-risk medications. Program evaluation: Patient data before and after implementation of the tool will be analysed to determine its effectiveness in reducing preoperative testing. Discussion Preoperative testing is an area that presents an opportunity to increase healthcare value and decrease healthcare spending. Guidelines are available to standardise preoperative assessment but their adoption and acceptance into practice has been slow. To systematise preoperative assessment within our healthcare system, we reviewed current published literature and guidelines and synthesised them into an electronic, evidence-based, decision-support tool. After distribution of the tool to clinicians in our healthcare system, we will assess its impact on healthcare value, costs and outcomes. We believe that an evidence-based preoperative tool, seamlessly and efficiently integrated into clinician workflow, can improve preoperative patient care.

scholarly journals Rationale and Design of an Evidence Based Tool to Guide Preoperative Evaluation and Management

2018 ◽  
Vol 2 (2) ◽  
pp. 16-21
Author(s):  
Alex Campbell ◽  
David Ingham ◽  
Joshua Mueller ◽  
Timothy Henry ◽  
Scott Sharkey ◽  
...  
Keyword(s):  
Decision Support ◽  
Preoperative Evaluation ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
The United States ◽  
Surgical Patient ◽  
Patient Harm ◽  
Excess Cost ◽  
Support Tool ◽  
Unique Approach

In the United States, over testing and over treatment are recognized as the cause of both excess cost and patient harm. Healthcare value (defined as “health outcomes achieved relative to the costs of care”) has become a focus to improve the quality and affordability of healthcare. Perioperative evaluation and management of the surgical patient represents a clear opportunity to improve healthcare value. Herein, we describe the rationale for and the development of a standardized clinical decision support tool that has been distributed to over 600 clinicians performing preoperative evaluations. All patients undergoing this evaluation will be tracked, with the intent to publish both healthcare cost and safety outcomes. The use of a perioperative decision support tool is a unique approach to value in healthcare.

scholarly journals Challenges involved in establishing a web-based clinical decision support tool in community health centers

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 100488
Author(s):  
Rachel Gold ◽  
Mary Middendorf ◽  
John Heintzman ◽  
Joan Nelson ◽  
Patrick O'Connor ◽  
...  
Keyword(s):  
Decision Support ◽  
Community Health ◽  
Clinical Decision Support ◽  
Community Health Centers ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Health Centers ◽  
Web Based ◽  
Support Tool ◽  
Clinical Decision Support Tool

scholarly journals Best Paper Selection

2018 ◽  
Vol 27 (01) ◽  
pp. 127-128
Keyword(s):  
Decision Support ◽  
Postoperative Delirium ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Knowledge Bases ◽  
Free Text ◽  
Health Records ◽  
Support Tool ◽  
Team Selection ◽  
Order Sets

Chen JH, Alagappan M, Goldstein MK, Asch SM, Altman RB. Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets. Int J Med Inform 2017 Jun;102:71-9 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28495350/ Ebadi A, Tighe PJ, Zhang L, Rashidi P. DisTeam: A decision support tool for surgical team selection. Artif Intell Med 2017 Feb;76:16-26 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28363285/ Fung KW, Kapusnik-Uner J, Cunningham J, Higby-Baker S, Bodenreider O. Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support. J Am Med Inform Assoc 2017 Jul 1;24(4):806-12 https://academic.oup.com/jamia/article-lookup/doi/10.1093/jamia/ocx010 Mikalsen KØ, Soguero-Ruiz C, Jensen K, Hindberg K, Gran M, Revhaug A, Lindsetmo RO, Skrøvseth SO, Godtliebsen F, Jenssen R. Using anchors from free text in electronic health records to diagnose postoperative delirium. Comput Methods Programs Biomed 2017 Dec;152:105-14 https://linkinghub.elsevier.com/retrieve/pii/S0169-2607(17)31154-9

scholarly journals Reduced Length of Stay Using Clinical Decision Support Tool (ASAP) for Empiric Antibiotic Selection

2020 ◽  
Vol 41 (S1) ◽  
pp. s368-s368
Author(s):  
Mary Acree ◽  
Kamaljit Singh ◽  
Urmila Ravichandran ◽  
Jennifer Grant ◽  
Gary Fleming ◽  
...  
Keyword(s):  
Decision Support ◽  
Length Of Stay ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Health Record ◽  
Antibiotic Selection ◽  
Support Tool ◽  
Empiric Antibiotic

Background: Empiric antibiotic selection is challenging and requires knowledge of the local antibiogram, national guidelines and patient-specific factors, such as drug allergy and recent antibiotic exposure. Clinical decision support for empiric antibiotic selection has the potential to improve adherence to guidelines and improve patient outcomes. Methods: At NorthShore University HealthSystem, a 4-hospital, 789 bed system, an automated point-of-care decision support tool referred to as Antimicrobial Stewardship Assistance Program (ASAP) was created for empiric antibiotic selection for 4 infectious syndromes: pneumonia, skin and soft-tissue infections, urinary tract infection, and intra-abdominal infection. The tool input data from the electronic health record, which can be modified by any user. Using an algorithm created with electronic health record data, antibiogram data, and national guidelines, the tool produces an antibiotic recommendation that can be ordered via a link to order entry. If the tool identifies a patient with a high likelihood for a multidrug-resistant infection, a consultation by an infectious diseases specialist is recommended. Utilization of the tool and associated outcomes were evaluated from July 2018 to May 2019. Results: The ASAP tool was executed by 140 unique, noninfectious diseases providers 790 times. The tool was utilized most often for pneumonia (194 tool uses), followed by urinary tract infection (166 tool uses). The most common provider type to use the tool was an internal medicine hospitalist. The tool increased adherence to the recommended antibiotic regimen for each condition. Antibiotic appropriateness was assessed by an infectious diseases physician. Antibiotics were considered appropriate when they were similar to the antibiotic regimen recommended by the ASAP. Inappropriate antibiotics were classified as broad or narrow. When antibiotic coverage was appropriate, hospital length of stay was statistically significantly shorter (4.8 days vs 6.8 days for broad antibiotics vs 7.4 days for narrow antibiotics; P < .01). No significant differences were identified in mortality or readmission. Conclusions: A clinical decision support tool in the electronic health record can improve adherence to recommended empiric antibiotic therapy. Use of appropriate antibiotics recommended by such a tool can reduce hospital length of stay.Funding: NoneDisclosures: None

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