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Supporting Clinical Decision Making in Cancer Care Delivery
Background: Cancer treatment and management require complicated clinical decision making to provide the highest quality of care for an individual patient. This is facilitated in part with ever-increasing availability of medications and treatments but hindered due to barriers such as access to care, cost of medications, clinician knowledge, and patient preferences or clinical factors. Although guidelines for cancer treatment and many symptoms have been developed to inform clinical practice, implementation of these guidelines into practice is often delayed or does not occur. Informatics-based approaches, such as clinical decision support, may be an effective tool to improve guideline implementation by delivering patient-specific and evidence-based knowledge to the clinician at the point of care to allow shared decision making with a patient and their family. The large amount of data in the electronic health record can be utilized to develop, evaluate, and implement automated approaches; however, the quality of the data must first be examined and evaluated.
Methods: This dissertation addresses gaps the literature about clinical decision making for cancer care delivery. Specifically, following an introduction and review of the literature for relevant topics to this dissertation, the researcher presents three studies. In Study One, the researcher explores the use of clinical decision support in cancer therapeutic decision making by conducting a systematic review of the literature. In Study Two, the researcher conducts a quantitative study to describe the rate of guideline concordant care provided for prevention of acute chemotherapy-induced nausea and vomiting (CINV) and to identify predictors of receiving guideline concordant care. In Study Three, the researcher conducts a mixed-methods study to evaluate the completeness, concordance, and heterogeneity of clinician documentation of CINV. The final chapter of this dissertation is comprised of key findings of each study, the strengths and limitations, clinical and research implications, and future research.
Results: In Study One, the systematic review, the researcher identified ten studies that prospectively studied clinical decision support systems or tools in a cancer setting to guide therapeutic decision making. There was variability in these studies, including study design, outcomes measured, and results. There was a trend toward benefit, both in process and patient-specific outcomes. Importantly, few studies were integrated into the electronic health record.
In Study Two, of 180 patients age 26 years or less, 36% received guideline concordant care as defined by pediatric or adult guidelines, as appropriate. Factors associated with receiving guideline concordant care included receiving a cisplatin-based regimen, being treated in adult oncology compared to pediatric oncology, and solid tumor diagnosis.
In Study Three, of the 127 patient records reviewed for the documentation of chemotherapy-induced nausea and vomiting, 75% had prescriber assessment documented and 58% had nursing assessment documented. Of those who had documented assessments by both prescriber and nurse, 72% were in agreement of the presence/absence of chemotherapy-induced nausea and vomiting. After mapping the concept through the United Medical Language System and developing a post-coordinated expression to identify chemotherapy-induced nausea and vomiting in the text, 85% of prescriber documentation and 100% of nurse documentation could be correctly categorized as present/absent. Further descriptors of the symptoms, such as severity or temporality, however, were infrequently reported.
Conclusion: In summary, this dissertation provides new knowledge about decision making in cancer care delivery. Specifically, in Study One the researcher describes that clinical decision support, one potential implementation strategy to improve guideline concordant care, is understudied or under published but a promising potential intervention. In Study Two, I identified factors that were associated with receipt of guideline concordant care for CINV, and these should be further explored to develop interventions. Finally, in Study Three, I report on the limitations of the data quality of CINV documentation in the electronic health record. Future work should focus on validating these results on a multi-institutional level
The Contribution of Patient Reported Outcome Measures to Shared Decision-Making in Radiation Oncology at a Midwestern Comprehensive Cancer Center
Background. Chronic diseases, such as lung cancer, require a provider-patient relationship developed over time. This relationship fosters shared decision-making (SDM), a collaborative, dynamic information exchange and analysis between provider and patient regarding treatment and desired outcomes. Established benefits to SDM include an improved quality of life and decreased anxiety and depression. Despite established benefits, recent research suggests radiation oncologists are not engaging in SDM. A decision-aid tool utilizing patient reported outcome measures may increase SDM between radiation oncologists and patients with lung cancer. Patient-reported outcome measures, wherein the patient provides direct assessment of their health and quality of life, can inform and initiate SDM. This study investigated the design and implementation of a collaborative decision-aid tool for patients with lung cancer at a Midwestern cancer center as informed by stakeholders, practice considerations, and the evidence base.
Objectives. The primary objective was to develop a collaborative decision-aid tool, using patient-reported outcome measures, that can be implemented in an academic radiation oncology clinic. Secondary objectives then assessed the tool’s impact through surrogates of shared decision making (add-on oncology visits, concomitant medication prescriptions), medical management (adverse events, radiation therapy compliance, chemotherapy compliance) and emergent care and its costs (emergency room visits and estimated costs, inpatient admissions and estimated costs). The hypothesized result was a decision aid designed to increase collaborative communication between radiation oncologists and patients will result in improved shared decision making, yielding better medical management and patient outcomes and reducing emergent care costs. Lastly, an implementation roadmap provided information on experienced barriers, facilitators, and considerations for performance objectives.
Materials and Methods. A sequential exploratory mixed methods design was employed. The qualitative strand explored how stakeholders, practice considerations, and the evidence base informed the design and installation of an ideal collaborative decision-aid tool. Semi-structured interviews were completed with both patients who completed radiation therapy for lung cancer and their radiation oncologist. Interviews were coded and evaluated for themes. Interviews were transcribed verbatim, coded using Atlas.ti software, and analyzed thematically and visually. The results of this analysis, combined with information from the literature base and implementation stakeholders, was used to inform design of the collaborative decision-aid tool that was installed employing the principles of clinical implementation using the plan-do-study-act (PDSA) implementation cycle model. Simple descriptive analysis was performed on objective measures. Mixed analysis included data display, comparison, and integration.
Results. Six patients and six radiation oncologists participated in the semi-structured interviews. Interviews provided insights that patients did not know what to ask of their radiation oncologists, prioritized survival over reduced side effects, and minimized complaints to their radiation oncologists, often to their detriment. Interviews yielded feedback on commonly used patient reported outcome instruments, identifying context as important and the recall timeframe as difficult. Commonly patient-identified adverse events of concern were fatigue, dyspnea, vomiting, and dysphagia. Radiation oncologists identified a patient’s personality as critical to care and translating responses and symptoms to adverse events of treatment. For this reason, numeric scales were not endorsed as they were seen as ambiguous and lacking context. With this feedback, a collaborative decision-aid tool was designed that focused on adverse events of interest (nausea, vomiting, fatigue, dyspnea, chest pain, weight loss). Rather than numeric scales, responses provided granular context that clued physicians to medical needs (i.e., “I cannot walk to my appointment,” “It hurts when I eat,” “I am not vomiting but I’m not hungry”). This tool was implemented as a quality initiative project for pragmatic impact. Four patients were assigned the tool during the first PDSA implementation cycle. The first follow-up evaluation meeting identified four critical outcomes for the next implementation cycle: how to identify which consults require the decision-aid, how the need for the decision-aid on doctor visits is consistently provided to scheduling, how unplanned visits/special complaints are addressed with regard to the decision-aid, and what actions are necessary if the patient leaves prior to the decision-aid being reviewed. Mixed analysis provided direction for next steps in implementation, tool design, and quantitative data measures. The primary concern, increase in time expended per clinic visit, was not supported by the limited data available from the first implementation cycle.
Conclusion. Implementation of collaborative decision-aid within the radiation oncology clinic is feasible without disruption of the on-treatment visit time. Radiation oncologists can use the tool as a guide for routine on-treatment visit review, so that it is harmonized with their routine practice. Care should be taken during implementation to ensure all stakeholders are included in the tool’s implementation and that desired outcomes are appropriately identified to truly capture what impact the tool has, if any, on clinical outcomes. Focusing on the patient with the goal of improving their experience will guide collaborative decision-aid tool adaptation, implementation, and uptake
Uncertainty in time-to-event distributions' parameters estimates in discrete event simulation models
Pediatric Acute Promyelocytic Leukemia and Fanconi Anemia: Case report and literature review
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