Pathways impact on OCM drug cost.

2019 ◽  
Vol 37 (27_suppl) ◽  
pp. 109-109
Author(s):  
Valerie Pracilio Csik ◽  
Jared Minetola ◽  
Karen Walsh ◽  
Michael J. Ramirez ◽  
Mark Hurwitz
Keyword(s):  
Stage Iv ◽  
Decision Support Tool ◽  
Healthcare Spending ◽  
Clinical Decision ◽  
Cancer Center ◽  
Support Tool ◽  
Oncology Care ◽  
The Difference ◽  
Stage Iv Lung Cancer ◽  
The Impact

109 Background: Oncology care, including drugs, represents a significant portion of US healthcare spending. Cost of Part B drugs has increased at a rate 5.7x that of overall Medicare spending (1997-2004). As a participant in the Oncology Care Model (OCM), we found drug costs represent a majority of our total costs. To reduce treatment (Tx) variability, our NCI-designated cancer center chose to implement pathways. Pathways are a clinical decision-support tool that use evidence-based care maps accounting for efficacy, toxicity and cost. At one institution, use of pathways contributed to $15k in savings for stage IV lung cancer Tx. We hypothesized pathway driven Tx standardization would favorably impact total chemotherapy (CTx) costs at the implementation site. Methods: In July 2018, we implemented pathways in Medical and Radiation Oncology for new starts or changes in Tx. Oncologists accessed the tool through our EMR, selected and placed orders for Tx. OCM quarterly data was used to compare 2 quarters immediately pre- and post-pathway implementation. The cancer-mix-adjusted Per-Member-Per-Month (PMPM) Allowed Amounts for CTx were compared between 3 groups; patients on-pathway, patients off-pathway and patients for which the pathways tool was not used (no utilization). PMPMs were evaluated pre- and post-implementation and an ANOVA test was used to evaluate significance of the difference between the two periods. Results: PMPM CTx costs decreased 4.6% between pre- and post-pathway implementation when oncologists followed pathways. By comparison, the off-pathway cohort and the no utilization groups had increases of 0.9% and 17.7% respectively. An evaluation of cost difference proved significant (p < .0001). Breast patients on-pathway had a cost decrease of 20%, compared to increases of 32% and 11% for off-pathway and no utilization groups, respectively. Conclusions: Pathway use reduced variation, a known contributor to healthcare costs, and therefore may be an effective cost control tool. Additional quarters of claims data is needed post-implementation to fully define the impact of pathways on total cost. [Table: see text]

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.

The value of pathways on drug costs.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 327-327
Author(s):  
Valerie Pracilio Csik ◽  
Michael J. Ramirez ◽  
Adam F Binder ◽  
Nathan Handley
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Weighted Average ◽  
Clinical Decision ◽  
Treatment Decisions ◽  
Drug Costs ◽  
Cancer Center ◽  
Significant Variable ◽  
Oncology Care ◽  
The Impact

327 Background: Oncology care represents a significant portion of US healthcare spending. Cost of Part B drugs has increased at a rate 5.7x that of overall Medicare spending. As a participant in the Oncology Care Model, drug costs represent a majority of our total costs. Pathways are a clinical decision-support tool that use evidence-based care maps accounting for efficacy, toxicity and cost. Our NCI-designated cancer center implemented pathways in July 2018 to reduce care variation and decrease costs. Methods: We reviewed costs related to pathway utilization over a two year period, analyzing differences in total annual drug cost for patients in three categories: On-Pathway (aligned with pathway recommendation), Off-Pathway (not aligned with recommendation), and No Pathway (not used). Per Member Per Month (PMPM) costs were calculated and a weighted average applied to account for changes in annual drug costs. Results: PMPM drug costs decreased -8% in year 1 (FY19) and -4% in year 2 (FY20) when pathways were used (On- and Off-Pathway). When pathways were followed (On-Pathway) in making treatment decisions, the drug costs were 11% lower than when pathways were not used. The annual impact on drug costs when pathways were used amounted to $2.45 million in year 1 and $1.77 million in year 2 (Table). Conclusions: Pathway use reduced drug costs, a significant variable in oncology value-based care models. This finding highlights the value of clinical decision support tools in reducing care variability, a known contributor to health care costs, in making treatment decisions. Further assessment is needed to determine if these results are similar at other cancer centers to fully realize the impact of pathways on drug costs.[Table: see text]

Impact of a clinical decision support tool targeting QT-prolonging medications

2020 ◽  
Vol 77 (Supplement_4) ◽  
pp. S111-S117
Author(s):  
Katie Chernoby ◽  
Michael F Lucey ◽  
Carrie L Hartner ◽  
Michelle Dehoorne ◽  
Stephanie B Edwin
Keyword(s):  
Risk Factors ◽  
Decision Support ◽  
Adverse Events ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Study Groups ◽  
Pharmacist Intervention ◽  
Support Tool ◽  
The Impact

Abstract Purpose To evaluate the impact of a newly implemented clinical decision support (CDS) tool targeting QT interval–prolonging medications on order verification and provider interventions. Methods A multicenter, retrospective quasi-experimental study was conducted to evaluate provider response to CDS alerts triggered during ordering of QT-prolonging medications for adult patients. The primary outcome was the proportion of orders triggering QTc alerts that were continued without intervention during a specified preimplementation phase (n = 49) and during a postimplementation phase (n = 100). Patient risk factors for QTc prolongation, provider alert response, and interventions to reduce the risk of QTc-associated adverse events were evaluated. Results The rate of order continuation without intervention was 82% in the preimplementation phase and 37% in the postimplementation phase, representing an 55% reduction in continued verified orders following implementation of the QT-focused CDS tool. Most alerts were initially responded to by the prescriber, with pharmacist intervention needed in only 33% of cases. There were no significant differences in patient QTc-related risk factors between the 2 study groups (P = 0.11); the postimplementation group had a higher proportion of patients using at least 2 QTc-prolonging medications (48%, compared to 26% in the preimplementation group; P = 0.02). Conclusion Implementation of the CDS tool was associated with a reduction in the proportion of orders continued without intervention in patients at high risk for QTc-related adverse events.

scholarly journals Impact of an electronic hard-stop clinical decision support tool to limit repeat Clostridioides difficile toxin enzyme immunoassay testing on test utilization

2019 ◽  
Vol 40 (12) ◽  
pp. 1423-1426 ◽  
Author(s):  
Jennie H. Kwon ◽  
Kimberly A. Reske ◽  
Tiffany Hink ◽  
Ronald Jackups ◽  
Carey-Ann D. Burnham ◽  
...  
Keyword(s):  
Decision Support ◽  
Enzyme Immunoassay ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Testing Rate ◽  
Clostridioides Difficile ◽  
Clinical Decision Support Tool ◽  
The Impact

AbstractWe performed an intervention evaluating the impact of an electronic hard-stop clinical decision support tool on repeat Clostridioides difficile (CD) toxin enzyme immunoassay (T-EIA) testing. The CD testing rate and number of admissions with repeat tests decreased significantly postintervention (P < .01 for both); the percentage of positive tests was unchanged (P = .27).

scholarly journals Evaluation of a clinical decision support tool for matching cancer patients to clinical trials using simulation-based research

2021 ◽  
Author(s):  
Clarissa Judith Gardner ◽  
Jack Halligan ◽  
Gianluca Fontana ◽  
Roberto Fernandez Crespo ◽  
Matthew Stewart Prime ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Decision Support ◽  
Cancer Patients ◽  
Clinical Decision Support ◽  
Digital Health ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Simulation Based ◽  
The Difference

Simulation-based research (SBR) methods have been proposed as an alternative methodology for evaluating digital health solutions; however, applicability remains to be established. This study used SBR to evaluate a clinical decision support (CDS) tool used for matching cancer patients to clinical trials. 25 clinicians and research staff were recruited to match 10 synthetic patient cases to clinical trials using both the CDS tool and publicly available online trial databases. Participants were significantly more likely to report having sufficient time (p = 0.020) and to require less mental effort (p = 0.001) to complete trial matching with the CDS tool. Participants required less time for trial matching using the CDS tool, but the difference was not significant (p = 0.093). Most participants reported that they had sufficient guidance to participate in the simulations (96%). This study demonstrates the use of SBR methods is a feasible approach to evaluating digital health solutions.

scholarly journals Implementation of a Clinical Decision Support Tool for Stool Cultures and Parasitological Studies in Hospitalized Patients

2017 ◽  
Vol 55 (12) ◽  
pp. 3350-3354 ◽  
Author(s):  
D. Nikolic ◽  
S. S. Richter ◽  
K. Asamoto ◽  
R. Wyllie ◽  
R. Tuttle ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Cost Savings ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Content Type ◽  
Support Tool ◽  
Clinical Decision Support Tool ◽  
The Impact ◽  
Stool Cultures

ABSTRACTThere is substantial evidence that stool culture and parasitological examinations are of minimal to no value after 3 days of hospitalization. We implemented and studied the impact of a clinical decision support tool (CDST) to decrease the number of unnecessary stool cultures (STCUL), ova/parasite (O&P) examinations, andGiardia/Cryptosporidiumenzyme immunoassay screens (GC-EIA) performed for patients hospitalized >3 days. We studied the frequency of stool studies ordered before or on day 3 and after day 3 of hospitalization (i.e., categorical orders/total number of orders) before and after this intervention and denoted the numbers and types of microorganisms detected within those time frames. This intervention, which corresponded to a custom-programmed hard-stop alert tool in the Epic hospital information system, allowed providers to override the intervention by calling the laboratory, if testing was deemed medically necessary. Comparative statistics were employed to determine significance, and cost savings were estimated based on our internal costs. Before the intervention, 129/670 (19.25%) O&P examinations, 47/204 (23.04%) GC-EIA, and 249/1,229 (20.26%) STCUL were ordered after 3 days of hospitalization. After the intervention, 46/521 (8.83%) O&P examinations, 27/157 (17.20%) GC-EIA, and 106/1,028 (10.31%) STCUL were ordered after 3 days of hospitalization. The proportions of reductions in the number of tests performed after 3 days and the associatedPvalues were 54.1% for O&P examinations (P< 0.0001), 22.58% for GC-EIA (P= 0.2807), and 49.1% for STCUL (P< 0.0001). This was estimated to have resulted in $8,108.84 of cost savings. The electronic CDST resulted in a substantial reduction in the number of evaluations of stool cultures and the number of parasitological examinations for patients hospitalized for more than 3 days and in a cost savings while retaining the ability of the clinician to obtain these tests if clinically indicated.

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