scholarly journals Evaluation of Multiple Myeloma Clinical Decision Support Tool Value When Populated with Community Health System Data

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 18-19
Author(s):  
Steven E. Labkoff ◽  
Kathy E. Giusti ◽  
Paul A. Giusti ◽  
Ryan Wilcox ◽  
Derrick Haslem ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Decision Support ◽  
Health System ◽  
Community Health ◽  
Clinical Decision Support ◽  
Outcomes Research ◽  
Treatment Guidelines ◽  
Conflicts Of Interest ◽  
Clinical Decision ◽  
Treatment Pathways

Introduction Clinical decision support (CDS) technology has the potential to improve health outcomes by offering physicians an informational resource to support review and application of best pratices.1 The Multiple Myeloma Research Foundation (MMRF) and Intermountain Healthcare (IMH) conducted a study to assess the suitability of a single health system's data for a myeloma-specific CDS tool that visualizes treatment pathways, and to assess the effort needed to support a CDS program.2 This research is part of a longer-term effort to explore how CDS technology can help: - increase awareness of and apply treatment guidelines by visualizing pathways for specific MM patient cohorts - improve understanding of treatment variation within health systems - improve outcomes research by showing relationships between treatments and outcomes Methods IA12 data from the CoMMpass study3 was used to create a CDS tool prototype. These data were aggregated into state and transition maps to identify nodes and pathways with corresponding outcomes, including response, progression-free survival (PFS), and overall survival (OS). Intervening patient states were displayed using Sankey diagrams [Fig. 1]. We also tested if EMR data from a community health system (i.e., IMH) could support such visualization. The team designed a study protocol and obtained IRB approval. Inclusion criteria included patients with active MM between January 2016-June 2018; adult aged 18 years to 89 years at diagnosis of active or smoldering MM. An IMH-specific data dictionary was assessed for variable importance, quantity, and ease of acquisition. [Table 1]. IMH then manually abstracted prioritized structured (eg: labs) and non-structured (eg: notes) data for use in the tool. Results Ninety-six of an initial 146 patients meeting eligibility criteria had sufficient data usable for the study, reflecting 44 unique drug combinations across 9 lines of therapy. The tool was able to associate and visualize all patients and their clinical states and transitions to their outcomes. Clinical data was typically complete (99% of the time), including key clinician-derived data, such as ECOG scores (78%) and treatment response (99%). 569 person-hours were required to conduct the abstraction activity on 96 cases, averaging 5.9 hours/patient Discussion The IMH portion of the study supports the hypothesis that a community health system can provide sufficient high-quality information to power a CDS tool with priority features. Only 65% (96/146) of the initial study group had usable data because some patients had received partial care outside of the IMH integrated delivery network (IDN) leaving associated data inaccessible. Initial biostatistical analysis suggests that roughly 750-1000 complete patient records would be required for statistically significant outcomes research with granularly stratified cohorts. The MMRF is currently recruiting 5-7 additional large IDNs to obtain the patients to power more generalizable functionality. References 1 McKie PM, Kor DJ, Cook DA, Kessler ME, Carter RE, Wilson PM, et al. Computerized advisory decision support for cardiovascular diseases in primary care: a cluster randomized trial. Am J Med [Internet]. 2019 Dec 18 [cited 2020 Mar 5]. Available from: https://doi.org/10.1016/j.amjmed.2019.10.039 2 Garcelon N, Burgun A, Salomon R, Neuraz A. Electronic health records for the diagnosis of rare diseases. Kidney Int [Internet]. 2020 Jan 14 [cited 2020 Mar 5]. Available from: https://doi.org/10.1016/ j.kint.2019.11.037 3 Christofferson A, Nasser S, Aldrich J, Penaherrera D, Legendre C, Benard B, et al. Integrative analysis of the genomic landscape underlying multiple myeloma at diagnosis: an Mmrf Commpass analysis. Blood. 2017 Dec 7; 130 (Supplement 1): 326. Disclosures No relevant conflicts of interest to declare.

Evaluation of multiple myeloma clinical decision support tool value for assessing treatment selection variation when populated with community health system data.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18781-e18781
Author(s):  
Steven E. Labkoff ◽  
Ryan Eldredge Wilcox ◽  
Ben Smith ◽  
Derrick S. Haslem ◽  
Daanish Hoda ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Decision Support ◽  
Health System ◽  
Community Health ◽  
Clinical Decision Support ◽  
Outcomes Research ◽  
Clinical Decision ◽  
Treatment Selection ◽  
System Data ◽  
Health System Data

e18781 Background: Clinical decision support (CDS) technology has the potential to improve health outcomes by offering physicians an informational resource to support review and application of best practices. The Multiple Myeloma Research Foundation (MMRF) and Intermountain Healthcare (IMH) conducted a study to assess the suitability of a single health system’s data for a myeloma-specific CDS tool that visualizes treatment pathways, and to assess the effort needed to support a CDS program. This research is part of a longer-term effort to explore how CDS technology can help: Increase awareness of and apply treatment guidelines by visualizing pathways for specific MM patient cohorts, Improve understanding of treatment variation for quality improvement within healthcare systems, Improve outcomes research by visualizing relationships between treatments and outcomes. This abstract focuses on the second use case, showing suitability of community health system data to assess treatment variability within the health system. Methods: IA12 data from the CoMMpass study was used to create a CDS tool prototype. These data were aggregated into state and transition maps to identify nodes and pathways with corresponding outcomes, including response, progression-free survival (PFS), and overall survival (OS). We also tested if EMR data from a community health system (i.e., IMH) could support such visualization. Inclusion criteria included patients with active MM between January 2016–June 2018; adult aged 18 years to 89 years at diagnosis of active or smoldering MM. An IMH-specific data dictionary was assessed for variable importance, quantity, and ease of acquisition. Results: Ninety-six of an initial 146 patients meeting eligibility criteria had sufficient data usable for the study, reflecting 44 unique drug combinations across 9 lines of therapy. The tool was able to associate and visualize all patients and their clinical states and transitions to their outcomes. Clinical data was typically complete (99% of the time), including key clinician-derived data, such as ECOG scores (78%) and treatment response (99%). Conclusions: The IMH portion of the study supports the hypothesis that a community health system can provide sufficiently high-quality information to power a CDS tool with priority features including display of treatment selection variability. Only 65% (96/146) of the initial study group had usable data because some patients had received partial care outside of the IMH integrated delivery network (IDN) leaving associated data inaccessible. Initial biostatistical analysis suggests that roughly 750-1000 complete patient records would be required for statistically significant outcomes research with granularly stratified cohorts. The MMRF plans to recruit additional large IDNs to obtain the patients to power more generalizable functionality.

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 A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea

2020 ◽  
Author(s):  
Jin-Hyeok Park ◽  
Jeong-Heum Baek ◽  
Sun Jin Sym ◽  
KangYoon Lee ◽  
Youngho Lee
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Decision Support ◽  
Cancer Treatment ◽  
Clinical Decision Support ◽  
Treatment Guidelines ◽  
Treatment Protocol ◽  
Clinical Decision ◽  
Actual Data ◽  
Nccn Guidelines

Abstract Background: Clinical Decision Support Systems (CDSSs) have recently attracted attention as a method for minimizing medical errors. Existing CDSSs are limited in that they do not reflect actual data. To overcome this limitation, we propose a CDSS based on deep learning. Methods: We propose the Colorectal Cancer Chemotherapy Recommender (C3R), which is a deep learning-based chemotherapy recommendation model. Our model improves on existing CDSSs in which data-based decision making is not well supported. C3R is configured to study the clinical data collected at the Gachon Gil Medical Center and to recommend appropriate chemotherapy based on the data. To validate the model, we compared the treatment concordance rate with the National Comprehensive Cancer Network (NCCN) Guidelines, a representative set of cancer treatment guidelines, and with the results of the Gachon Gil Medical Center’s Colorectal Cancer Treatment Protocol (GCCTP). Results: For the CR3 model, the treatment concordance rates with the NCCN guidelines were 70.5% for Top-1 Accuracy and 84% for Top-2 Accuracy. The treatment concordance rates with the GCCTP were 57.9% for Top-1 Accuracy and 77.8% for Top-2 Accuracy. Conclusions: This model is significant, i.e., it is the first colon cancer treatment clinical decision support system in Korea that reflects actual data. In the future, if sufficient data can be secured through cooperation among multiple organizations, more reliable results can be obtained.

Organizational Factors Influencing the Use of Clinical Decision Support for Improving Cancer Screening Within Community Health Centers

2016 ◽  
pp. 118-148 ◽  
Author(s):  
Timothy Jay Carney ◽  
Michael Weaver ◽  
Anna M. McDaniel ◽  
Josette Jones ◽  
David A. Haggstrom
Keyword(s):  
Colorectal Cancer ◽  
Health Care ◽  
Decision Support ◽  
Cancer Screening ◽  
Community Health ◽  
Clinical Decision Support ◽  
Community Health Centers ◽  
Clinical Decision ◽  
Health Centers ◽  
Practice Setting

Adoption of clinical decision support (CDS) systems leads to improved clinical performance through improved clinician decision making, adherence to evidence-based guidelines, medical error reduction, and more efficient information transfer and to reduction in health care disparities in under-resourced settings. However, little information on CDS use in the community health care (CHC) setting exists. This study examines if organizational, provider, or patient level factors can successfully predict the level of CDS use in the CHC setting with regard to breast, cervical, and colorectal cancer screening. This study relied upon 37 summary measures obtained from the 2005 Cancer Health Disparities Collaborative (HDCC) national survey of 44 randomly selected community health centers. A multi-level framework was designed that employed an all-subsets linear regression to discover relationships between organizational/practice setting, provider, and patient characteristics and the outcome variable, a composite measure of community health center CDS intensity-of-use. Several organizational and provider level factors from our conceptual model were identified to be positively associated with CDS level of use in community health centers. The level of CDS use (e.g., computerized reminders, provider prompts at point-of-care) in support of breast, cervical, and colorectal cancer screening rate improvement in vulnerable populations is determined by both organizational/practice setting and provider factors. Such insights can better facilitate the increased uptake of CDS in CHCs that allows for improved patient tracking, disease management, and early detection in cancer prevention and control within vulnerable populations.

scholarly journals A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jin-Hyeok Park ◽  
Jeong-Heum Baek ◽  
Sun Jin Sym ◽  
Kang Yoon Lee ◽  
Youngho Lee
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Decision Support ◽  
Cancer Treatment ◽  
Clinical Decision Support ◽  
Treatment Guidelines ◽  
Treatment Protocol ◽  
Clinical Decision ◽  
Actual Data ◽  
Nccn Guidelines

Abstract Background Clinical Decision Support Systems (CDSSs) have recently attracted attention as a method for minimizing medical errors. Existing CDSSs are limited in that they do not reflect actual data. To overcome this limitation, we propose a CDSS based on deep learning. Methods We propose the Colorectal Cancer Chemotherapy Recommender (C3R), which is a deep learning-based chemotherapy recommendation model. Our model improves on existing CDSSs in which data-based decision making is not well supported. C3R is configured to study the clinical data collected at the Gachon Gil Medical Center and to recommend appropriate chemotherapy based on the data. To validate the model, we compared the treatment concordance rate with the National Comprehensive Cancer Network (NCCN) Guidelines, a representative set of cancer treatment guidelines, and with the results of the Gachon Gil Medical Center’s Colorectal Cancer Treatment Protocol (GCCTP). Results For the C3R model, the treatment concordance rates with the NCCN guidelines were 70.5% for Top-1 Accuracy and 84% for Top-2 Accuracy. The treatment concordance rates with the GCCTP were 57.9% for Top-1 Accuracy and 77.8% for Top-2 Accuracy. Conclusions This model is significant, i.e., it is the first colon cancer treatment clinical decision support system in Korea that reflects actual data. In the future, if sufficient data can be secured through cooperation among multiple organizations, more reliable results can be obtained.

scholarly journals Clinical implementation of pharmacogenomics via a health system-wide research biobank: the University of Colorado experience

2020 ◽  
Vol 21 (6) ◽  
pp. 375-386 ◽  
Author(s):  
Christina L Aquilante ◽  
David P Kao ◽  
Katy E Trinkley ◽  
Chen-Tan Lin ◽  
Kristy R Crooks ◽  
...  
Keyword(s):  
Decision Support ◽  
Health System ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Clinical Implementation ◽  
Support Tool ◽  
University Of Colorado ◽  
The University ◽  
The Impact

In recent years, the genomics community has witnessed the growth of large research biobanks, which collect DNA samples for research purposes. Depending on how and where the samples are genotyped, biobanks also offer the potential opportunity to return actionable genomic results to the clinical setting. We developed a preemptive clinical pharmacogenomic implementation initiative via a health system-wide research biobank at the University of Colorado. Here, we describe how preemptive return of clinical pharmacogenomic results via a research biobank is feasible, particularly when coupled with strong institutional support to maximize the impact and efficiency of biobank resources, a multidisciplinary implementation team, automated clinical decision support tools, and proactive strategies to engage stakeholders early in the clinical decision support tool development process.

scholarly journals Comparative outcome studies of clinical decision support software: limitations to the practice of evidence-based system acquisition

2015 ◽  
Vol 22 (e1) ◽  
pp. e13-e20 ◽  
Author(s):  
Gaurav Jay Dhiman ◽  
Kyle T Amber ◽  
Kenneth W. Goodman
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Conflicts Of Interest ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Sound Studies ◽  
Outcome Studies ◽  
Evidence Based ◽  
System Acquisition ◽  
Support Software

Abstract Clinical decision support systems (CDSSs) assist clinicians with patient diagnosis and treatment. However, inadequate attention has been paid to the process of selecting and buying systems. The diversity of CDSSs, coupled with research obstacles, marketplace limitations, and legal impediments, has thwarted comparative outcome studies and reduced the availability of reliable information and advice for purchasers. We review these limitations and recommend several comparative studies, which were conducted in phases; studies conducted in phases and focused on limited outcomes of safety, efficacy, and implementation in varied clinical settings. Additionally, we recommend the increased availability of guidance tools to assist purchasers with evidence-based purchases. Transparency is necessary in purchasers’ reporting of system defects and vendors’ disclosure of marketing conflicts of interest to support methodologically sound studies. Taken together, these measures can foster the evolution of evidence-based tools that, in turn, will enable and empower system purchasers to make wise choices and improve the care of patients.

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