Going off pathway: Problem or good care?

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 7014-7014
Author(s):  
Stephen B. Edge ◽  
Lu Liu ◽  
Nessa Stefaniak ◽  
Monica L. Murphy ◽  
James E. Thompson ◽  
...  
Keyword(s):  
Decision Support ◽  
Multidisciplinary Team ◽  
Financial Incentives ◽  
Drug Toxicity ◽  
Metastatic Cancer ◽  
Cancer Center ◽  
Free Text ◽  
New Drug ◽  
Co Morbidity ◽  
Provide Decision Support

7014 Background: Clinical oncology pathways (COP) provide decision support and benchmarking against national standards. Some organizations provide financial incentives for using COP-recommended treatment (on pathway: OnP). Treatment (Rx) other than COP recommended Rx (off pathway: OffP) is appropriate for some cases. There are limited data on the appropriateness of OffP Rx. This study examines rates and reasons for OffP Rx in one cancer center. Methods: All systemic Rx decisions entered in the ClinicalPath COP from 10/1/18 - 9/30/19 were classified as OnP (including Rx on a clinical trial) or OffP and as adjuvant/neoadjuvant therapy (ADJ) or for metastatic cancer (MET). Oncologists must provide free text reasons for OffP Rx. Records of all OffP care were reviewed by a senior nurse-led team and physician to verify and classify OffP reasons. Cases without clear documentation were referred to the treating oncologist and/or multidisciplinary team for review. Justified OffP reasons (R1-6) were classified as: R1. Documented drug toxicity and/or treatment-limiting co-morbidity; R2. Prior treatment precluding pathway Rx; R3. New drug indication or molecular targeted therapy not in COP; R4. Continuation of Rx started prior to referral; R5. Other clearly documented and reviewed provider or multidisciplinary team rationale; and R6. Patient preference. Results: There were 2,997 COP treatment decisions for 2,389 patients. The OnP rate was higher for ADJ than for MET Rx (87% vs. 78%). Non-justified OffP care accounted for 1% of cases. 69% of OffP Rx was because of known drug toxicity, co-morbidity limiting therapy, prior therapy precluding COP choice, and new drug indications (Table). Conclusions: COPs provide decision support and practice benchmarking. Lower OnP rates for MET Rx likely reflect the nuances of Rx for advanced cancer. Most OffP care was justified and appropriate. Financial incentives that focus on the percentage of COP OnP care could paradoxically harm the quality of care, especially given the high percentage of OffP decisions for reasons of drug toxicity, co-morbidity and new drug indications. [Table: see text]

scholarly journals Contributions on Clinical Decision Support from the 2018 Literature

2019 ◽  
Vol 28 (01) ◽  
pp. 135-137 ◽  
Author(s):  
Vassilis Koutkias ◽  
Jacques Bouaud ◽  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Care Delivery ◽  
Metastatic Cancer ◽  
Clinical Decision ◽  
Data Driven ◽  
Editorial Team ◽  
Cancer Center ◽  
Bibliographic Databases ◽  
Free Text

Objectives: To summarize recent research and select the best papers published in 2018 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook. Methods: A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and the section editors' evaluation. Results: Among 1,148 retrieved articles, 15 best paper candidates were selected, the review of which resulted in the selection of four best papers. The first paper introduces a deep learning model for estimating short-term life expectancy (>3 months) of metastatic cancer patients by analyzing free-text clinical notes in electronic medical records, while maintaining the temporal visit sequence. The second paper takes note that CDSSs become routinely integrated in health information systems and compares statistical anomaly detection models to identify CDSS malfunctions which, if remain unnoticed, may have a negative impact on care delivery. The third paper fairly reports on lessons learnt from the development of an oncology CDSS using artificial intelligence techniques and from its assessment in a large US cancer center. The fourth paper implements a preference learning methodology for detecting inconsistencies in clinical practice guidelines and illustrates the applicability of the proposed methodology to antibiotherapy. Conclusions: Three of the four best papers rely on data-driven methods, and one builds on a knowledge-based approach. While there is currently a trend for data-driven decision support, the promising results of such approaches still need to be confirmed by the adoption of these systems and their routine use.

scholarly journals Best Paper Selection

2019 ◽  
Vol 28 (01) ◽  
pp. 138-139
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Decision Support ◽  
Cancer Patients ◽  
Clinical Decision Support ◽  
Metastatic Cancer ◽  
Clinical Decision ◽  
Preference Learning ◽  
Free Text ◽  
Statistical Anomaly Detection

Banerjee I, Gensheimer MF, Wood DJ, Henry S, Aggarwal S, Chang DT, Rubin DL. Probabilistic prognostic estimates of survival in metastatic cancer patients (PPES-Met) utilizing free-text clinical narratives. Sci Rep 2018 Jul 3;8(1):10037 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030075/ Ray S, McEvoy DS, Aaron S, Hickman TT, Wright A. Using statistical anomaly detection models to find clinical decision support malfunctions. J Am Med Inform Assoc 2018 Jul 1;25(7):862-71 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6016695/ Simon G, DiNardo CD, Takahashi K, Cascone T, Powers C, Stevens R, Allen J, Antonoff MB, Gomez D, Keane P, Suarez Saiz F, Nguyen Q, Roarty E, Pierce S, Zhang J, Hardeman Barnhill E, Lakhani K, Shaw K, Smith B, Swisher S, High R, Futreal PA, Heymach, Chin L. Applying Artificial Intelligence to address the knowledge gaps in cancer care. Oncologist 2018 Nov 16 pii: theoncologist.2018-0257 http://theoncologist.alphamedpress.org/content/24/6/772.long Tsopra R, Lamy JB, Sedki K. Using preference learning for detecting inconsistencies in clinical practice guidelines: methods and application to antibiotherapy. Artif Intell Med 2018 Jul;89:24-33 https://www.sciencedirect.com/science/article/pii/S0933365718300873?via%3Dihub

scholarly journals Best Paper Selection

2018 ◽  
Vol 27 (01) ◽  
pp. 127-128
Keyword(s):  
Decision Support ◽  
Postoperative Delirium ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Knowledge Bases ◽  
Free Text ◽  
Health Records ◽  
Support Tool ◽  
Team Selection ◽  
Order Sets

Chen JH, Alagappan M, Goldstein MK, Asch SM, Altman RB. Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets. Int J Med Inform 2017 Jun;102:71-9 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28495350/ Ebadi A, Tighe PJ, Zhang L, Rashidi P. DisTeam: A decision support tool for surgical team selection. Artif Intell Med 2017 Feb;76:16-26 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28363285/ Fung KW, Kapusnik-Uner J, Cunningham J, Higby-Baker S, Bodenreider O. Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support. J Am Med Inform Assoc 2017 Jul 1;24(4):806-12 https://academic.oup.com/jamia/article-lookup/doi/10.1093/jamia/ocx010 Mikalsen KØ, Soguero-Ruiz C, Jensen K, Hindberg K, Gran M, Revhaug A, Lindsetmo RO, Skrøvseth SO, Godtliebsen F, Jenssen R. Using anchors from free text in electronic health records to diagnose postoperative delirium. Comput Methods Programs Biomed 2017 Dec;152:105-14 https://linkinghub.elsevier.com/retrieve/pii/S0169-2607(17)31154-9

Decision Support System for Radiotherapy Patient Scheduling: Thai Cancer Center Case Study

2021 ◽  
Author(s):  
Chawis Boonmee ◽  
Nirand Pisutha-Arnond ◽  
Wichai Chattinnawat ◽  
Pooriwat Muangwong ◽  
Wannapha Nobnop ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Cancer Center ◽  
Patient Scheduling

Contemporary outcomes for adults with AML requiring ICU admission.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 7025-7025
Author(s):  
Danielle Hammond ◽  
Koji Sasaki ◽  
Alexis Geppner ◽  
Fadi Haddad ◽  
Shehab Mohamed ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Cancer Center ◽  
Hospital Death ◽  
Entire Cohort ◽  
Morbidity Burden ◽  
Icu Admission ◽  
Poor Risk ◽  
Line Therapy ◽  
Co Morbidity ◽  
Life Threatening

7025 Background: Patients (pts) with AML frequently encounter life-threatening complications requiring transfer to an intensive care unit (ICU). Methods: Retrospective analysis of 145 adults with AML requiring ICU admission at our tertiary cancer center 2018-19. Use of life-sustaining therapies (LSTs) and overall survival (OS) were reported using descriptive statistics. Logistic regression was used to identify risk factors for in-hospital death. Results: Median age was 64 yrs (range 18-86). 47% of pts had an ECOG status of ≥ 2 with a median of at least 1 comorbidity (Table). 117 pts (81%) had active leukemia at admission. 68 pts (47%) had poor-risk cytogenetics (CG) and 32 (22%) had TP53-mutated disease. 61 (42%), 27 (19%) and 57 pts (39%) were receiving 1st, 2nd and ≥ 3rd line therapy. 33 (23%) and 70 pts (48%) were receiving intensive and lower-intensity chemotherapy, respectively, and 77 pts (53%) were concurrently on venetoclax. Most common indications for admission were sepsis (32%), respiratory failure (24%) and leukocytosis (12%); Table outlines additional ICU admission details. Median OS from the date of ICU admission was 2.0 months (mo) for the entire cohort and 6.9, 1.6 and 1.2 mo in pts with favorable-, intermediate- and poor-risk CG. Median OS of pts receiving frontline vs. ≥ 2nd line therapy was 4.2 vs. 1.4 mo (P<0.001). Median OS in pts requiring 0-1 vs. 2-3 LSTs was 4.1 vs. 0.4 mo (P<0.001). OS was not different by age, co-morbidity burden nor therapy intensity. In a multivariate analysis that included SOFA scores, only adverse CG (OR 0.35, P = 0.028), and need for intubation with mechanical ventilation (IMV; OR 0.19, P = 0.009) were associated with increased odds of in-hospital mortality. Conclusions: A substantial portion of pts with AML survive their ICU admission with sufficient functionality to return home and receive subsequent therapy. In contrast to general medical populations, age, co-morbidities, and SOFA scores were not independently predictive of in-hospital mortality. Disease CG risk and the need for IMV were the strongest predictors of ICU survival. This suggests that many pts with AML can benefit from ICU care.[Table: see text]

scholarly journals Cranky comments: detecting clinical decision support malfunctions through free-text override reasons

2018 ◽  
Vol 26 (1) ◽  
pp. 37-43 ◽  
Author(s):  
Skye Aaron ◽  
Dustin S McEvoy ◽  
Soumi Ray ◽  
Thu-Trang T Hickman ◽  
Adam Wright
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Naive Bayes ◽  
Word List ◽  
Clinical Decision ◽  
Naïve Bayes ◽  
Free Text ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

Abstract Background Rule-base clinical decision support alerts are known to malfunction, but tools for discovering malfunctions are limited. Objective Investigate whether user override comments can be used to discover malfunctions. Methods We manually classified all rules in our database with at least 10 override comments into 3 categories based on a sample of override comments: “broken,” “not broken, but could be improved,” and “not broken.” We used 3 methods (frequency of comments, cranky word list heuristic, and a Naïve Bayes classifier trained on a sample of comments) to automatically rank rules based on features of their override comments. We evaluated each ranking using the manual classification as truth. Results Of the rules investigated, 62 were broken, 13 could be improved, and the remaining 45 were not broken. Frequency of comments performed worse than a random ranking, with precision at 20 of 8 and AUC = 0.487. The cranky comments heuristic performed better with precision at 20 of 16 and AUC = 0.723. The Naïve Bayes classifier had precision at 20 of 17 and AUC = 0.738. Discussion Override comments uncovered malfunctions in 26% of all rules active in our system. This is a lower bound on total malfunctions and much higher than expected. Even for low-resource organizations, reviewing comments identified by the cranky word list heuristic may be an effective and feasible way of finding broken alerts. Conclusion Override comments are a rich data source for finding alerts that are broken or could be improved. If possible, we recommend monitoring all override comments on a regular basis.

scholarly journals A Closer Look at the “Right” Format for Clinical Decision Support: Methods for Evaluating a Storyboard BestPractice Advisory

2020 ◽  
Vol 10 (4) ◽  
pp. 142
Author(s):  
Brian J. Douthit ◽  
R. Clayton Musser ◽  
Kay S. Lytle ◽  
Rachel L. Richesson
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Local Level ◽  
Clinical Decision ◽  
System Level ◽  
Evaluation Methodology ◽  
Task Completion ◽  
Free Text ◽  
Alert Fatigue ◽  
The Impact

(1) Background: The five rights of clinical decision support (CDS) are a well-known framework for planning the nuances of CDS, but recent advancements have given us more options to modify the format of the alert. One-size-fits-all assessments fail to capture the nuance of different BestPractice Advisory (BPA) formats. To demonstrate a tailored evaluation methodology, we assessed a BPA after implementation of Storyboard for changes in alert fatigue, behavior influence, and task completion; (2) Methods: Data from 19 weeks before and after implementation were used to evaluate differences in each domain. Individual clinics were evaluated for task completion and compared for changes pre- and post-redesign; (3) Results: The change in format was correlated with an increase in alert fatigue, a decrease in erroneous free text answers, and worsened task completion at a system level. At a local level, however, 14% of clinics had improved task completion; (4) Conclusions: While the change in BPA format was correlated with decreased performance, the changes may have been driven primarily by the COVID-19 pandemic. The framework and metrics proposed can be used in future studies to assess the impact of new CDS formats. Although the changes in this study seemed undesirable in aggregate, some positive changes were observed at the level of individual clinics. Personalized implementations of CDS tools based on local need should be considered.

scholarly journals CDS in a Learning Health Care System: Identifying Physicians' Reasons for Rejection of Best-Practice Recommendations in Pneumonia through Computerized Clinical Decision Support

2019 ◽  
Vol 10 (01) ◽  
pp. 001-009 ◽  
Author(s):  
Barbara Jones ◽  
Dave Collingridge ◽  
Caroline Vines ◽  
Herman Post ◽  
John Holmen ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Health Care System ◽  
Clinical Decision Support ◽  
Best Practice ◽  
Clinical Decision ◽  
Additional Patient ◽  
Free Text ◽  
Learning Health Care System ◽  
Care System

Background Local implementation of guidelines for pneumonia care is strongly recommended, but the context of care that affects implementation is poorly understood. In a learning health care system, computerized clinical decision support (CDS) provides an opportunity to both improve and track practice, providing insights into the implementation process. Objectives This article examines physician interactions with a CDS to identify reasons for rejection of guideline recommendations. Methods We implemented a multicenter bedside CDS for the emergency department management of pneumonia that integrated patient data with guideline-based recommendations. We examined the frequency of adoption versus rejection of recommendations for site-of-care and antibiotic selection. We analyzed free-text responses provided by physicians explaining their clinical reasoning for rejection, using concept mapping and thematic analysis. Results Among 1,722 patient episodes, physicians rejected recommendations to send a patient home in 24%, leaving text in 53%; reasons for rejection of the recommendations included additional or alternative diagnoses beyond pneumonia, and comorbidities or signs of physiologic derangement contributing to risk of outpatient failure that were not processed by the CDS. Physicians rejected broad-spectrum antibiotic recommendations in 10%, leaving text in 76%; differences in pathogen risk assessment, additional patient information, concern about antibiotic properties, and admitting physician preferences were given as reasons for rejection. Conclusion While adoption of CDS recommendations for pneumonia was high, physicians rejecting recommendations frequently provided feedback, reporting alternative diagnoses, additional individual patient characteristics, and provider preferences as major reasons for rejection. CDS that collects user feedback is feasible and can contribute to a learning health system.

scholarly journals Measuring Risk Allocation of Tax Burden for Small and Micro Enterprises

2019 ◽  
Vol 11 (3) ◽  
pp. 741 ◽  
Author(s):  
Bing Xu ◽  
Lili Li ◽  
Yan Liang ◽  
Mohib Rahman
Keyword(s):  
Decision Support ◽  
Path Model ◽  
Insurance Fund ◽  
Tax Burden ◽  
Risk Allocation ◽  
Time Varying ◽  
Credit Insurance ◽  
Provide Decision Support ◽  
Micro Enterprises

Tax burden outlier inhibits the growth of small and micro enterprises. This paper introduces the risk allocation of tax burden to measure the tax burden outlier. Using a time-varying nonparametric benchmark and path model, this paper measures the tax risk allocation of 3552 small and micro enterprises in the credit insurance fund from January 2016 to August 2018. This paper explores the configuration of tax burden risk allocation and discusses the changes along time and with the addition of other variables. Finally, this paper gives an analysis of strategies to improve tax burden risk allocation. The results provide decision support for reducing the tax burden and promoting the growth of small and micro enterprises.

scholarly journals Oncology Fellow–Led Quality Improvement Project to Improve Rates of Palliative Care Utilization in Patients With Advanced Cancer

2020 ◽  
Vol 16 (8) ◽  
pp. e814-e822 ◽  
Author(s):  
Ramy Sedhom ◽  
Arjun Gupta ◽  
Mirat Shah ◽  
Melinda Hsu ◽  
Marcus Messmer ◽  
...  
Keyword(s):  
Palliative Care ◽  
Quality Improvement ◽  
Advanced Cancer ◽  
Metastatic Cancer ◽  
Cancer Center ◽  
Care Utilization ◽  
First Year ◽  
Improvement Project ◽  
Leadership Support ◽  
Ancillary Staff

PURPOSE: ASCO guidelines recommend palliative care (PC) referral for patients with advanced or metastatic cancer. Despite this, implementation has considerable hurdles. First-year oncology fellows at our institution identified low rates of PC utilization in their longitudinal clinic as a metric needing improvement. METHODS: A fellow-led multidisciplinary team aimed to increase PC utilization for patients with advanced cancer followed in he first-year fellows’ clinic from a baseline of 11.5% (5 of 43 patients, July to December of 2018) to 30% over a 6-month period. Utilization was defined as evaluation in the outpatient PC clinic hosted in the cancer center. The team identified the following barriers to referral: orders difficult to find in the electronic medical record (EMR), multiple consulting mechanisms (EMR, by phone, or in person), EMR request not activating formal consult, no centralized scheduler to contact or confirm appointment, and poor awareness of team structure. Plan-Do-Study-Act (PDSA) cycles were implemented based on identified opportunities. Data were obtained from the EMR. RESULTS: The first PDSA cycle included focus groups with stakeholders, standardizing referral process via single order set, identifying a single scheduler with bidirectional communication, and disseminating process changes. PDSA cycles were implemented from January to June of 2019. Rates of PC use increased from 11.5% before the intervention to 48.4% (48 of 99 patients) after the intervention. CONCLUSION: A multidisciplinary approach and classic quality improvement methodology improved PC use in patients with advanced cancer. The pilot succeeded given the small number of fellows, buy-in from stakeholders, and institutional and leadership support. Straightforward EMR interventions and ancillary staff use are effective in addressing underreferrals.

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