scholarly journals Cancer Multidisciplinary Team Meetings: Evidence, Challenges, and the Role of Clinical Decision Support Technology

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Vivek Patkar ◽  
Dionisio Acosta ◽  
Tim Davidson ◽  
Alison Jones ◽  
John Fox ◽  
...  
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Multidisciplinary Team ◽  
Care Delivery ◽  
Clinical Decision ◽  
Full Potential ◽  
Team Meetings ◽  
The Status ◽  
The Impact

Multidisciplinary team (MDT) model in cancer care was introduced and endorsed to ensure that care delivery is consistent with the best available evidence. Over the last few years, regular MDT meetings have become a standard practice in oncology and gained the status of the key decision-making forum for patient management. Despite the fact that cancer MDT meetings are well accepted by clinicians, concerns are raised over the paucity of good-quality evidence on their overall impact. There are also concerns over lack of the appropriate support for this important but overburdened decision-making platform. The growing acceptance by clinical community of the health information technology in recent years has created new opportunities and possibilities of using advanced clinical decision support (CDS) systems to realise full potential of cancer MDT meetings. In this paper, we present targeted summary of the available evidence on the impact of cancer MDT meetings, discuss the reported challenges, and explore the role that a CDS technology could play in addressing some of these challenges.

Driving quality improvement: How clinical decision support can facilitate compliance with evidence-based pathways.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 2045-2045
Author(s):  
Debra A. Patt ◽  
Bo He ◽  
Jody S. Garey ◽  
Paul Rowan ◽  
Michael D Swartz ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Cancer Care ◽  
Care Delivery ◽  
Clinical Decision ◽  
Clinical Informatics ◽  
Evidence Based ◽  
Large Network ◽  
The Impact

2045 Background: Cancer care is changing rapidly with more detailed understanding of disease and more numerous therapeutic choices. As treatment choice is more complex, mechanisms to improve compliance with evidence based treatment can improve the quality of cancer care. Methods: A retrospective cohort study was conducted from January 2014-May 2016 evaluating the impact of a clinical decision support system (CDSS) on compliance with evidence based pathways (EBP) across 9 statewide community based oncology practices. These EBP are developed with physician input on efficacy toxicity and value and incorporated in to a CDSS that is used within the Electronic Health Record (EHR) at point of care to alter the choice architecture a clinician sees when prescribing therapy. A multi-level logistic regression model was used to adjust for group effects on physician or practice behavior. SAS 9.4 software was used and GLIMMIX was applied. Individual physician benchmark compliance was evaluated using McNemar's test. Results: Regimen compliance with EBP was measured pre- and post- implementation of the CDSS tool across a large network encompassing 9 statewide practices and 633 physicians who prescribed over 30,000 individual patient treatment regimens over a 6 month period. The CDSS that is incorporated within the EHR significantly improved compliance with EBP across the entire cohort of practices, and in individual practices (see Table). Individual oncologists reached a target of 75% compliance more often (58% vs 72%) after implementation of the tool (p < 0.001). Conclusions: CDSS is a tool that improves compliance with EBP that is effective at improving targets of compliance broadly, at the practice, and at the individual clinician level. Clinical informatics solutions that influence physician behavior can be inclusive of physicians in design, iterative in process, and nudge as opposed to force clinician behavior to drive quality improvement. These clinical informatics solutions grow in importance as the complexity of cancer care continues to increase and we seek to improve upon the quality and value of care delivery. [Table: see text]

scholarly journals Barriers and Facilitators for Implementation of a Computerized Clinical Decision Support System in Lung Cancer Multidisciplinary Team Meetings—A Qualitative Assessment

Biology ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 9 ◽  
Author(s):  
Sosse E. Klarenbeek ◽  
Olga C. J. Schuurbiers-Siebers ◽  
Michel M. van den Heuvel ◽  
Mathias Prokop ◽  
Marcia Tummers
Keyword(s):  
Lung Cancer ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Multidisciplinary Team ◽  
Clinical Decision ◽  
Barriers And Facilitators ◽  
Easy Access ◽  
Successful Implementation ◽  
Team Meetings ◽  
Multidisciplinary Team Meetings

Background: Oncological computerized clinical decision support systems (CCDSSs) to facilitate workflows of multidisciplinary team meetings (MDTMs) are currently being developed. To successfully implement these CCDSSs in MDTMs, this study aims to: (a) identify barriers and facilitators for implementation for the use case of lung cancer; and (b) provide actionable findings for an implementation strategy. Methods: The Consolidated Framework for Implementation Science was used to create an interview protocol and to analyze the results. Semi-structured interviews were conducted among various health care professionals involved in MDTMs. The transcripts were analyzed using a thematic analysis following a deductive approach. Results: Twenty-six professionals participated in the interviews. The main facilitators for implementation of the CCDSS were considered to be easy access to well-structured patient data, and the resulting reduction of MDTM preparation time and of duration of MDTMs. Main barriers for adoption were seen in incomplete or non-trustworthy output generated by the system and insufficient adaptability of the system to local and contextual needs. Conclusion: Using a CCDSS in lung cancer MDTMs was expected to increase efficiency of workflows. Successful implementation was seen as dependent on the reliability and adaptability of the CCDSS and involvement of key users in the implementation process.

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.

scholarly journals Clinical Decision Support for Ovarian Carcinoma Subtype Classification: A Pilot Observer Study With Pathology Trainees

2019 ◽  
Vol 144 (7) ◽  
pp. 869-877 ◽  
Author(s):  
Marios A. Gavrielides ◽  
Meghan Miller ◽  
Ian S. Hagemann ◽  
Heba Abdelal ◽  
Zahra Alipour ◽  
...  
Keyword(s):  
Decision Support ◽  
Ovarian Carcinoma ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Subspecialty Training ◽  
Reference Standard ◽  
Observer Study ◽  
Subtype Classification ◽  
The Impact ◽  
Whole Slide Images

Context.— Clinical decision support (CDS) systems could assist less experienced pathologists with certain diagnostic tasks for which subspecialty training or extensive experience is typically needed. The effect of decision support on pathologist performance for such diagnostic tasks has not been examined. Objective.— To examine the impact of a CDS tool for the classification of ovarian carcinoma subtypes by pathology trainees in a pilot observer study using digital pathology. Design.— Histologic review on 90 whole slide images from 75 ovarian cancer patients was conducted by 6 pathology residents using: (1) unaided review of whole slide images, and (2) aided review, where in addition to whole slide images observers used a CDS tool that provided information about the presence of 8 histologic features important for subtype classification that were identified previously by an expert in gynecologic pathology. The reference standard of ovarian subtype consisted of majority consensus from a panel of 3 gynecologic pathology experts. Results.— Aided review improved pairwise concordance with the reference standard for 5 of 6 observers by 3.3% to 17.8% (for 2 observers, increase was statistically significant) and mean interobserver agreement by 9.2% (not statistically significant). Observers benefited the most when the CDS tool prompted them to look for missed histologic features that were definitive for a certain subtype. Observer performance varied widely across cases with unanimous and nonunanimous reference classification, supporting the need for balancing data sets in terms of case difficulty. Conclusions.— Findings showed the potential of CDS systems to close the knowledge gap between pathologists for complex diagnostic tasks.

scholarly journals The Changing Nature of Clinical Decision Support Systems: a Focus on Consumers, Genomics, Public Health and Decision Safety

2009 ◽  
Vol 18 (01) ◽  
pp. 84-95 ◽  
Author(s):  
A. Y. S. Lau ◽  
G. Tsafnat ◽  
V. Sintchenko ◽  
F. Magrabi ◽  
E. Coiera
Keyword(s):  
Public Health ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Genetic Data ◽  
Clinical Decision ◽  
Translational Bioinformatics ◽  
The Impact

Summary Objectives To review the recent research literature in clinical decision support systems (CDSS). Methods A review of recent literature was undertaken, focussing on CDSS evaluation, consumers and public health, the impact of translational bioinformatics on CDSS design, and CDSS safety. Results In recent years, researchers have concentrated much less on the development of decision technologies, and have focussed more on the impact of CDSS in the clinical world. Recent work highlights that traditional process measures of CDSS effectiveness, such as document relevance are poor proxy measures for decision outcomes. Measuring the dynamics of decision making, for example via decision velocity, may produce a more accurate picture of effectiveness. Another trend is the broadening of user base for CDSS beyond front line clinicians. Consumers are now a major focus for biomedical informatics, as are public health officials, tasked with detecting and managing disease outbreaks at a health system, rather than individual patient level. Bioinformatics is also changing the nature of CDSS. Apart from personalisation of therapy recommendations, translational bioinformatics is creating new challenges in the interpretation of the meaning of genetic data. Finally, there is much recent interest in the safety and effectiveness of computerised physicianorderentry (CPOE) systems, given that prescribing and administration errors are a significant cause of morbidity and mortality. Of note, there is still much controversy surrounding the contention that poorly designed, implemented or used CDSS may actually lead to harm. Conclusions CDSS research remains an active and evolving area of research, as CDSS penetrate more widely beyond their traditional domain into consumer decision support, and as decisions become more complex, for example by involving sequence level genetic data.

Characterizing Workflow to Inform Clinical Decision Support Systems in Nursing Homes

2018 ◽  
Vol 59 (6) ◽  
pp. 1024-1033 ◽  
Author(s):  
Mustafa Ozkaynak ◽  
Blaine Reeder ◽  
Cynthia Drake ◽  
Peter Ferrarone ◽  
Barbara Trautner ◽  
...  
Keyword(s):  
Decision Support ◽  
Data Analysis ◽  
Nursing Homes ◽  
Clinical Decision Support ◽  
Care Delivery ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Staff Members ◽  
Design And Implementation ◽  
Multiple Levels

Abstract Background and Objectives Clinical decision support systems (CDSS) hold promise to influence clinician behavior at the point of care in nursing homes (NHs) and improving care delivery. However, the success of these interventions depends on their fit with workflow. The purpose of this study was to characterize workflow in NHs and identify implications of workflow for the design and implementation of CDSS in NHs. Research Design and Methods We conducted a descriptive study at 2 NHs in a metropolitan area of the Mountain West Region of the United States. We characterized clinical workflow in NHs, conducting 18 observation sessions and interviewing 15 staff members. A multilevel work model guided our data collection and framework method guided data analysis. Results The qualitative analysis revealed specific aspects of multilevel workflow in NHs: (a) individual, (b) work group/unit, (c) organization, and (d) industry levels. Data analysis also revealed several additional themes regarding workflow in NHs: centrality of ongoing relationships of staff members with the residents to care delivery in NHs, resident-centeredness of care, absence of memory aids, and impact of staff members’ preferences on work activities. We also identified workflow-related differences between the two settings. Discussion and Implications Results of this study provide a rich understanding of the characteristics of workflow in NHs at multiple levels. The design of CDSS in NHs should be informed by factors at multiple levels as well as the emergent processes and contextual factors. This understanding can allow for incorporating workflow considerations into CDSS design and implementation.

“Send & hold” clinical decision support rules improvement to reduce unnecessary testing of vitamins A, E, K, B1, B2, B3, B6 and C

2018 ◽  
Vol 56 (7) ◽  
pp. 1063-1070 ◽  
Author(s):  
Enrique Rodriguez-Borja ◽  
Africa Corchon-Peyrallo ◽  
Esther Barba-Serrano ◽  
Celia Villalba Martínez ◽  
Arturo Carratala Calvo
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Primary Care Physicians ◽  
Vitamin B1 ◽  
Clinical Decision ◽  
Health Department ◽  
Leadership Role ◽  
Control Group ◽  
Vitamin B3 ◽  
The Impact

Abstract Background: We assessed the impact of several “send & hold” clinical decision support rules (CDSRs) within the electronical request system for vitamins A, E, K, B1, B2, B3, B6 and C for all outpatients at a large health department. Methods: When ordered through electronical request, providers (except for all our primary care physicians who worked as a non-intervention control group) were always asked to answer several compulsory questions regarding main indication, symptomatology, suspected diagnosis, vitamin active treatments, etc., for each vitamin test using a drop-down list format. After samples arrival, tests were later put on hold internally by our laboratory information system (LIS) until review for their appropriateness was made by two staff pathologists according to the provided answers and LIS records (i.e. “send & hold”). The number of tests for each analyte was compared between the 10-month period before and after CDSRs implementation in both groups. Results: After implementation, vitamins test volumes decreased by 40% for vitamin A, 29% for vitamin E, 42% for vitamin K, 37% for vitamin B1, 85% for vitamin B2, 68% for vitamin B3, 65% for vitamin B6 and 59% for vitamin C (all p values 0.03 or lower except for vitamin B3), whereas in control group, the majority increased or remained stable. In patients with rejected vitamins, no new requests and/or adverse clinical outcome comments due to this fact were identified. Conclusions: “Send & hold” CDSRs are a promising informatics tool that can support in utilization management and enhance the pathologist’s leadership role as tests specialist.

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