scholarly journals AMDIS Case Conference

2010 ◽  
Vol 01 (01) ◽  
pp. 68-78 ◽  
Author(s):  
D. Levick ◽  
R. Schreiber ◽  
J. Graham
Keyword(s):  
Patient Safety ◽  
Decision Support ◽  
Best Practices ◽  
Clinical Decision Support ◽  
Point Of Care ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Case Conference ◽  
Alerting System

SummaryClinical decision support that provides enhanced patient safety at the point of care frequently encounters significant pushback from clinicians who find the process intrusive or time-consuming. We present a hypothetical medical center’s dilemma about its allergy alerting system and discuss similar problems faced by real hospitals. We then share some lessons learned and best practices for institutions who wish to implement these tools themselves.

From Adverse Drug Event Detection to Prevention

2014 ◽  
Vol 53 (06) ◽  
pp. 482-492 ◽  
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.

scholarly journals Integrating the Practical Robust Implementation and Sustainability Model With Best Practices in Clinical Decision Support Design: Implementation Science Approach

10.2196/19676 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e19676
Author(s):  
Katy E Trinkley ◽  
Michael G Kahn ◽  
Tellen D Bennett ◽  
Russell E Glasgow ◽  
Heather Haugen ◽  
...  
Keyword(s):  
Decision Support ◽  
Best Practices ◽  
Implementation Science ◽  
Clinical Decision Support ◽  
Integrated Approach ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Support Design ◽  
Robust Implementation ◽  
Sustainability Model

Background Clinical decision support (CDS) design best practices are intended to provide a narrative representation of factors that influence the success of CDS tools. However, they provide incomplete direction on evidence-based implementation principles. Objective This study aims to describe an integrated approach toward applying an existing implementation science (IS) framework with CDS design best practices to improve the effectiveness, sustainability, and reproducibility of CDS implementations. Methods We selected the Practical Robust Implementation and Sustainability Model (PRISM) IS framework. We identified areas where PRISM and CDS design best practices complemented each other and defined methods to address each. Lessons learned from applying these methods were then used to further refine the integrated approach. Results Our integrated approach to applying PRISM with CDS design best practices consists of 5 key phases that iteratively interact and inform each other: multilevel stakeholder engagement, designing the CDS, design and usability testing, thoughtful deployment, and performance evaluation and maintenance. The approach is led by a dedicated implementation team that includes clinical informatics and analyst builder expertise. Conclusions Integrating PRISM with CDS design best practices extends user-centered design and accounts for the multilevel, interacting, and dynamic factors that influence CDS implementation in health care. Integrating PRISM with CDS design best practices synthesizes the many known contextual factors that can influence the success of CDS tools, thereby enhancing the reproducibility and sustainability of CDS implementations. Others can adapt this approach to their situation to maximize and sustain CDS implementation success.

scholarly journals Integrating the Practical Robust Implementation and Sustainability Model With Best Practices in Clinical Decision Support Design: Implementation Science Approach (Preprint)

2020 ◽  
Author(s):  
Katy E Trinkley ◽  
Michael G Kahn ◽  
Tellen D Bennett ◽  
Russell E Glasgow ◽  
Heather Haugen ◽  
...  
Keyword(s):  
Decision Support ◽  
Best Practices ◽  
Implementation Science ◽  
Clinical Decision Support ◽  
Integrated Approach ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Support Design ◽  
Robust Implementation ◽  
Sustainability Model

BACKGROUND Clinical decision support (CDS) design best practices are intended to provide a narrative representation of factors that influence the success of CDS tools. However, they provide incomplete direction on evidence-based implementation principles. OBJECTIVE This study aims to describe an integrated approach toward applying an existing implementation science (IS) framework with CDS design best practices to improve the effectiveness, sustainability, and reproducibility of CDS implementations. METHODS We selected the Practical Robust Implementation and Sustainability Model (PRISM) IS framework. We identified areas where PRISM and CDS design best practices complemented each other and defined methods to address each. Lessons learned from applying these methods were then used to further refine the integrated approach. RESULTS Our integrated approach to applying PRISM with CDS design best practices consists of 5 key phases that iteratively interact and inform each other: multilevel stakeholder engagement, designing the CDS, design and usability testing, thoughtful deployment, and performance evaluation and maintenance. The approach is led by a dedicated implementation team that includes clinical informatics and analyst builder expertise. CONCLUSIONS Integrating PRISM with CDS design best practices extends user-centered design and accounts for the multilevel, interacting, and dynamic factors that influence CDS implementation in health care. Integrating PRISM with CDS design best practices synthesizes the many known contextual factors that can influence the success of CDS tools, thereby enhancing the reproducibility and sustainability of CDS implementations. Others can adapt this approach to their situation to maximize and sustain CDS implementation success.

Lessons learned from the development of the CancerLinQ prototype: Clinical decision support.

2013 ◽  
Vol 31 (31_suppl) ◽  
pp. 237-237
Author(s):  
Richard L. Schilsky ◽  
Sandra M. Swain ◽  
Robert Hauser ◽  
Joshua Mann ◽  
George W. Sledge ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Point Of Care ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Learning System ◽  
Unified Medical Language System ◽  
Platform Process ◽  
Share Data ◽  
Rules Engine

237 Background: CancerLinQ (CLQ) is a rapid learning system (RLS) for oncology in development by ASCO. CLQ is based on the transfer of electronic health records (EHR) from participating oncology practices to a data warehouse where data aggregation and de-identification occurs. A prototype was built using open source software and has collected de-identified data on 170,000+ pts with breast cancer (BC) from 31 community oncology practices using 4 different EHRs. The primary goals for the prototype were 1. Aggregate patient data from any EHR platform, process it and create a longitudinal record; 2. Develop quality reports from EHRs; 3. Point of care Clinical Decision Support (CDS) from ASCO guidelines; 4. Data visualization for hypothesis generation; 5. Demonstrate desire to share data for quality improvement; 6. Describe lessons learned (LL). This report focuses on LL about CDS. Methods: Physician experts identified specific elements from each ASCO BC guideline to make machine readable (MR). Abstractors then GEM-cut the elements using the GEM Abstraction Manual and Style Guide. The output reports were reviewed for comprehensiveness, accuracy, and style. Following verification of the GEM-cut content, reports were sent for meta-tagging, done by selecting widely used EHR vocabulary from the Unified Medical Language System (UMLS). The GEM-cut output and meta-tags were converted to DROOLS syntax and the resulting coded files were inserted into the DROOLS rules engine. When the rules engine encounters a combination of facts that match a rule, that rule is presented to the user. The enduring responses are collected using ‘queries’ and the CDS results are delivered to the EHR. Results: Guidelines are often not written as “if”/“then” statements which is key for computer-based CDS. Any unintentional ambiguity must be removed for machine MR CDS. Using new methodologies, we have been able to convert narrative guidelines into MR CDS. Conclusions: Conversion of ASCO’s clinical guidelines into a MR format is possible. New and emerging methods such as GLIDES, BRIDGE-Wiz, and GEM-cutting provide excellent tools to migrate existing narrative recommendations into MR format that can populate CDS tools, such as those provided by CancerLinQ.

Malignant hyperthermia susceptibility: utilization of genetic results in an electronic medical record to increase safety

2020 ◽  
Vol 21 (17) ◽  
pp. 1207-1215
Author(s):  
Jordan F Baye ◽  
Natasha J Petry ◽  
Shauna L Jacobson ◽  
Michelle M Moore ◽  
Bethany Tucker ◽  
...  
Keyword(s):  
Decision Support ◽  
Malignant Hyperthermia ◽  
Clinical Decision Support ◽  
Perioperative Complications ◽  
Implementation Strategies ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Malignant Hyperthermia Susceptibility ◽  
Alert System ◽  
Genotyping Platform

Aim: This manuscript describes implementation of clinical decision support for providers concerned with perioperative complications of malignant hyperthermia susceptibility. Materials & methods: Clinical decision support for malignant hyperthermia susceptibility was implemented in 2018 based around our pre-emptive genotyping platform. We completed a brief descriptive review of patients who underwent pre-emptive testing, focused particularly on RYR1 and CACNA1S genes. Results: To date, we have completed pre-emptive genetic testing on more than 10,000 patients; 13 patients having been identified as a carrier of a pathogenic or likely pathogenic variant of RYR1 or CACNA1S. Conclusion: An alert system for malignant hyperthermia susceptibility – as an extension of our pre-emptive genomics platform – was implemented successfully. Implementation strategies and lessons learned are discussed herein.

scholarly journals A Narrative Review of Clinical Decision Support for Inpatient Clinical Pharmacists

2021 ◽  
Vol 12 (02) ◽  
pp. 199-207
Author(s):  
Liang Yan ◽  
Thomas Reese ◽  
Scott D. Nelson
Keyword(s):  
Patient Safety ◽  
Quality Of Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Controlled Trials ◽  
Clinical Pharmacists ◽  
Randomized Controlled ◽  
The Impact

Abstract Objective Increasingly, pharmacists provide team-based care that impacts patient care; however, the extent of recent clinical decision support (CDS), targeted to support the evolving roles of pharmacists, is unknown. Our objective was to evaluate the literature to understand the impact of clinical pharmacists using CDS. Methods We searched MEDLINE, EMBASE, and Cochrane Central for randomized controlled trials, nonrandomized trials, and quasi-experimental studies which evaluated CDS tools that were developed for inpatient pharmacists as a target user. The primary outcome of our analysis was the impact of CDS on patient safety, quality use of medication, and quality of care. Outcomes were scored as positive, negative, or neutral. The secondary outcome was the proportion of CDS developed for tasks other than medication order verification. Study quality was assessed using the Newcastle–Ottawa Scale. Results Of 4,365 potentially relevant articles, 15 were included. Five studies were randomized controlled trials. All included studies were rated as good quality. Of the studies evaluating inpatient pharmacists using a CDS tool, four showed significantly improved quality use of medications, four showed significantly improved patient safety, and three showed significantly improved quality of care. Six studies (40%) supported expanded roles of clinical pharmacists. Conclusion These results suggest that CDS can support clinical inpatient pharmacists in preventing medication errors and optimizing pharmacotherapy. Moreover, an increasing number of CDS tools have been developed for pharmacists' roles outside of order verification, whereby further supporting and establishing pharmacists as leaders in safe and effective pharmacotherapy.

Real-Time Clinical Decision Support at the Point of Care

2019 ◽  
pp. 125-133
Author(s):  
Ana Margarida Pereira ◽  
Cristina Jácome ◽  
Rita Amaral ◽  
Tiago Jacinto ◽  
João A Fonseca
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Clinical Decision Support ◽  
Point Of Care ◽  
Clinical Decision

Red Book Point-of-Care Clinical Decision Support Tool

2018 ◽  
Keyword(s):  
Decision Support ◽  
Infectious Diseases ◽  
Clinical Decision Support ◽  
Health Care Professionals ◽  
Point Of Care ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Pediatric Health Care ◽  
Flip Chart

This convenient flip chart provides pediatric health care professionals with point-of-care guidance on the assessment, prevention, and treatment of childhood infectious diseases. https://shop.aap.org/red-book-pediatric-infectious-diseases-clinical-decision-support-chart/

scholarly journals Implementation of clinical decision support to manage acute kidney injury in secondary care: an ethnographic study

2019 ◽  
Vol 29 (5) ◽  
pp. 382-389
Author(s):  
Simon Bailey ◽  
Carianne Hunt ◽  
Adam Brisley ◽  
Susan Howard ◽  
Lynne Sykes ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Patient Safety ◽  
Quality Improvement ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Kidney Injury ◽  
Clinical Decision ◽  
Ethnographic Study ◽  
Routine Practice ◽  
Trade Offs

BackgroundOver the past decade, acute kidney injury (AKI) has become a global priority for improving patient safety and health outcomes. In the UK, a confidential inquiry into AKI led to the publication of clinical guidance and a range of policy initiatives. National patient safety directives have focused on the mandatory establishment of clinical decision support systems (CDSSs) within all acute National Health Service (NHS) trusts to improve the detection, alerting and response to AKI. We studied the organisational work of implementing AKI CDSSs within routine hospital care.MethodsAn ethnographic study comprising non-participant observation and interviews was conducted in two NHS hospitals, delivering AKI quality improvement programmes, located in one region of England. Three researchers conducted a total of 49 interviews and 150 hours of observation over an 18-month period. Analysis was conducted collaboratively and iteratively around emergent themes, relating to the organisational work of technology adoption.ResultsThe two hospitals developed and implemented AKI CDSSs using very different approaches. Nevertheless, both resulted in adaptive work and trade-offs relating to the technology, the users, the organisation and the wider system of care. A common tension was associated with attempts to maximise benefit while minimise additional burden. In both hospitals, resource pressures exacerbated the tensions of translating AKI recommendations into routine practice.ConclusionsOur analysis highlights a conflicted relationship between external context (policy and resources), and organisational structure and culture (eg, digital capability, attitudes to quality improvement). Greater consideration is required to the long-term effectiveness of the approaches taken, particularly in light of the ongoing need for adaptation to incorporate new practices into routine work.

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