Clinical Decision Support and Optional Point of Care Testing of Renal Function for Safe Use of Antibiotics in Elderly Patients: A Retrospective Study in Community Pharmacy Practice

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1074. Evaluation of a Pharmacist-led Antimicrobial and Anticoagulant Monitoring Initiative

Open Forum Infectious Diseases ◽

10.1093/ofid/ofz360.938 ◽

2019 ◽

Vol 6 (Supplement_2) ◽

pp. S380-S381

Author(s):

Wei Hsiang Lin ◽

Amanda Binkley ◽

Christo L Cimino ◽

Naasha J Talati ◽

Jimish M Mehta ◽

...

Keyword(s):

Renal Function ◽

Decision Support ◽

Decision Support System ◽

Clinical Decision Support ◽

Support System ◽

Clinical Decision Support System ◽

Dose Adjustment ◽

Adverse Drug Events ◽

Medical Center ◽

Clinical Decision

Abstract Background Adverse drug events are associated with an increase in hospital stay and cost. Risks from these events are minimized by adjusting a medication’s dose or frequency, and changes in renal function may necessitate adjustments. Currently, there is no formal procedure for a prospective audit of renal function over the weekend at our institution. This pharmacist-driven initiative will evaluate if a prospective review identified by real-time clinical decision support alerts over the weekend will reduce the time from change in renal function to dose adjustment of select antimicrobials and/or anticoagulants. Methods This monitoring initiative is comprised of a pre- and post-cohort population. The pre-cohort population included patients admitted to Penn Presbyterian Medical Center (PPMC) from January to March of 2018 on select antimicrobials and/or anticoagulants, who were identified to have a change in renal function (serum creatinine change of 0.3 mg/dL or greater) over the weekend. The post-cohort population was identified with a clinical decision support system (ILÚM Health Solutions, Kenilworth, NJ) and included patients admitted to PPMC from January to March of 2019. A pharmacy resident reviewed alerts in the clinical decision support system over the weekend and contacted providers with dose adjustment recommendations. The Mann–Whitney U test was used to analyze the primary endpoint while descriptive statistics were used for the secondary endpoints Results Eighteen interventions were completed within the 3-month post-cohort intervention period, with a time to dose adjustment between the pre/post-cohort being reduced by 50 hours (P = 0.0001) resulting in a median time to change of 11 hours in the post-cohort. All pharmacy recommendations were accepted by the provider, and 94% of medication adjustments were antimicrobials. Conclusion The application of this prospective weekend initiative utilizing a clinical decision support system demonstrated a clinically and statistically significant reduction in the time to dose adjustments for antimicrobials and/or anticoagulants. Implementation of this initiative will further establish a role for pharmacist-led evaluations and could potentially be expanded to other clinical areas. Disclosures All authors: No reported disclosures.

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A systematic review of near real-time and point-of-care clinical decision support in anesthesia information management systems

Journal of Clinical Monitoring and Computing ◽

10.1007/s10877-016-9921-x ◽

2016 ◽

Vol 31 (5) ◽

pp. 885-894 ◽

Cited By ~ 13

Author(s):

Allan F. Simpao ◽

Jonathan M. Tan ◽

Arul M. Lingappan ◽

Jorge A. Gálvez ◽

Sherry E. Morgan ◽

...

Keyword(s):

Systematic Review ◽

Decision Support ◽

Real Time ◽

Information Management ◽

Clinical Decision Support ◽

Point Of Care ◽

Clinical Decision ◽

Management Systems ◽

Information Management Systems

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Current Status and Future Prospects for Electronic Point-of-Care Clinical Decision Support in Diabetes Care

Current Diabetes Reports ◽

10.1007/s11892-012-0350-z ◽

2012 ◽

Vol 13 (2) ◽

pp. 172-176 ◽

Cited By ~ 7

Author(s):

Patrick J. O’Connor ◽

Jay R. Desai ◽

John C. Butler ◽

Elyse O. Kharbanda ◽

JoAnn M. Sperl-Hillen

Keyword(s):

Decision Support ◽

Clinical Decision Support ◽

Diabetes Care ◽

Point Of Care ◽

Clinical Decision ◽

Current Status ◽

Future Prospects

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From Adverse Drug Event Detection to Prevention

Methods of Information in Medicine ◽

10.3414/me14-01-0027 ◽

2014 ◽

Vol 53 (06) ◽

pp. 482-492 ◽

Cited By ~ 6

Author(s):

P. McNair ◽

V. Kilintzis ◽

K. Skovhus Andersen ◽

J. Niès ◽

J.-C. Sarfati ◽

...

Keyword(s):

Patient Safety ◽

Decision Support ◽

Clinical Decision Support ◽

Adverse Drug Event ◽

Support Services ◽

Medication Safety ◽

Point Of Care ◽

Clinical Decision ◽

Substantial Contribution ◽

Drug Event

Summary Background: Errors related to medication seriously affect patient safety and the quality of healthcare. It has been widely argued that various types of such errors may be prevented by introducing Clinical Decision Support Systems (CDSSs) at the point of care. Objectives: Although significant research has been conducted in the field, still medication safety is a crucial issue, while few research outcomes are mature enough to be considered for use in actual clinical settings. In this paper, we present a clinical decision support framework targeting medication safety with major focus on adverse drug event (ADE) prevention. Methods: The novelty of the framework lies in its design that approaches the problem holistically, i.e., starting from knowledge discovery to provide reliable numbers about ADEs per hospital or medical unit to describe their consequences and probable causes, and next employing the acquired knowledge for decision support services development and deployment. Major design features of the frame-work’s services are: a) their adaptation to the context of care (i.e. patient characteristics, place of care, and significance of ADEs), and b) their straightforward integration in the healthcare information technologies (IT) infrastructure thanks to the adoption of a service-oriented architecture (SOA) and relevant standards. Results: Our results illustrate the successful interoperability of the framework with two commercially available IT products, i.e., a Computerized Physician Order Entry (CPOE) and an Electronic Health Record (EHR) system, respectively, along with a Web prototype that is independent of existing health-care IT products. The conducted clinical validation with domain experts and test cases illustrates that the impact of the framework is expected to be major, with respect to patient safety, and towards introducing the CDSS functionality in practical use. Conclusions: This study illustrates an important potential for the applicability of the presented framework in delivering contextualized decision support services at the point of care and for making a substantial contribution towards ADE prevention. None-theless, further research is required in order to quantitatively and thoroughly assess its impact in medication safety.

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Clinical Decision Support Tool and Rapid Point-of-Care Platform for Determining Disease Severity in Patients with COVID-19

10.1101/2020.04.16.20068411 ◽

2020 ◽

Cited By ~ 3

Author(s):

Michael P. McRae ◽

Glennon W. Simmons ◽

Nicolaos J. Christodoulides ◽

Zhibing Lu ◽

Stella K. Kang ◽

...

Keyword(s):

Decision Support ◽

Disease Severity ◽

Clinical Decision Support ◽

Severity Score ◽

Troponin I ◽

Point Of Care ◽

Clinical Decision ◽

Adverse Outcomes ◽

Number Of Patients ◽

Severity Scores

AbstractSARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase–myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40–83) and 9 (6–17), respectively, and area under the curve of 0.94 (95% CI 0.89– 0.99). These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

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Clinical Decision Support Intervention Decreases Time to Imaging in Elderly Patients with Traumatic Brain Injuries

Journal of the American College of Surgeons ◽

10.1016/j.jamcollsurg.2019.08.636 ◽

2019 ◽

Vol 229 (4) ◽

pp. S291

Author(s):

Brian V. Thielen ◽

Simon Yang ◽

Arthur Nguyen ◽

Regina Martinez Lorenzo ◽

Kristina Techar ◽

...

Keyword(s):

Decision Support ◽

Elderly Patients ◽

Clinical Decision Support ◽

Brain Injuries ◽

Clinical Decision ◽

Traumatic Brain Injuries ◽

Support Intervention ◽

Decision Support Intervention

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Point-of-care clinical decision support for cancer symptom management: Results of a group randomized trial.

Journal of Clinical Oncology ◽

10.1200/jco.2014.32.31_suppl.1 ◽

2014 ◽

Vol 32 (31_suppl) ◽

pp. 1-1 ◽

Cited By ~ 2

Author(s):

Mary E. Cooley ◽

Traci Blonquist ◽

Paul Catalano ◽

David Lobach ◽

Ilana Braun ◽

...

Keyword(s):

Palliative Care ◽

Decision Support ◽

Clinical Decision Support ◽

Symptom Management ◽

Point Of Care ◽

Symptom Assessment ◽

Well Being ◽

Clinical Decision ◽

Group Randomized Trial ◽

Significant Difference

1 Background: Integration of palliative care into oncology is recommended for quality care. Clinicians may benefit from assistance in assessing and managing multiple symptoms. Palliative care clinicians have the expertise but may not be available or are not consulted early in the course of a patient’s disease. Clinical decision support (CDS) offers an innovative way to deliver symptom management and trigger palliative care referrals at the point-of-care. Methods: Twenty clinicians and their patients were randomized to usual care (UC) or CDS using the symptom assessment and management intervention (SAMI), which provided tailored suggestions for pain, fatigue, depression, anxiety and/or dyspnea. One-hundred seventy-nine patients completed a Web-based symptom assessment prior to each visit for 6 months. A tailored report provided a longitudinal symptom report and suggestions for management were provided to clinicians in the SAMI arm prior to the visit. Standardized questionnaires were administered to patients at baseline, 2, 4 and 6 months later to measure communication about symptoms and health-related quality of life (HR-QOL). The treatment outcome index (TOI) was the primary outcome for HR-QOL. Management of the target symptoms was assessed through chart review. Linear mixed models and logistic regression were used for analyses. Results: Patient characteristics were: mean age of 63 years, 58% female, 88% white, and 32% had < HS education. No differences were noted in communication between patients and their clinicians. Significant differences were noted in physical well-being (p = 0.007, 0.08 adjusted for baseline) and a clinically significant difference in the TOI (62 vs. 68) at 4 months in SAMI as compared to UC. The odds of managing depression (1.6, 90% CI, 1.0-2.5), anxiety (1.7, 90% CI, 1.0-3.0) and fatigue (1.6, 90% CI, 1.1-2.5) were higher in SAMI as compared to UC. The odds of palliative care consults for pain (3.2, 90% CI, 0.7-13.4) appear to be higher in SAMI as compared to UC. Conclusions: Enhanced HR-QOL was noted among patients in the SAMI arm at 4 months. SAMI increased management of depression, fatigue and anxiety and appeared to increase palliative care consults for pain. Clinical trial information: NCT00852462.

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