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Biomedical subjects

Karen Sepucha

Publications and source records attributed to Karen Sepucha.

6 recordsLinked to original sources
PubMed2006

An approach to measuring the quality of breast cancer decisions.

OBJECTIVE: To explore an approach to measuring the quality of decisions made in the treatment of early stage breast cancer, focusing on patients' decision-specific knowledge and the concordance between patients' stated preferences for treatment outcomes and treatment received. METHODS: Candidate knowledge and value items were identified after an extensive review of the published literature as well as reports on 27 focus groups and 46 individual interviews with breast cancer survivors. Items were subjected to cognitive interviews with six additional patients. A preliminary decision quality measure consisting of five knowledge items and four value items was pilot tested with 35 breast cancer survivors who also completed the control preferences scale and the decisional conflict scale (DCS). RESULTS: Preference for control and knowledge did not vary by treatment. The mean of the participants' knowledge scores was 54%. There was no correlation between the knowledge scores and the informed subscale of the DCS (Pearson r = .152, n = 32, p = 0.408). Patients who preferred to keep their breast were over five times as likely to have breast-conserving surgery than those who did not (OR 5.33, 95% CI (1.2, 24.5), p = 0.06). Patients who wanted to avoid radiation were six times as likely to choose mastectomy than those who did not (OR 6.4, 95% CI (1.34, 30.61), p = 0.04). CONCLUSION: Measuring decision quality by assessing patients' decision-specific knowledge and concordance between their values and treatment received, is feasible and important. Further work is necessary to overcome the methodological challenges identified in this pilot work. PRACTICE IMPLICATIONS: Guidelines for early stage breast cancer emphasize the importance of including patients' preferences in decisions about treatment. The ability of doctors and patients to make decisions that reflect the considered preferences of well-informed patients can and should be measured.

Adult↗
PubMed2006

Developing a quality criteria framework for patient decision aids: online international Delphi consensus process.

OBJECTIVE: To develop a set of quality criteria for patient decision support technologies (decision aids). DESIGN AND SETTING: Two stage web based Delphi process using online rating process to enable international collaboration. PARTICIPANTS: Individuals from four stakeholder groups (researchers, practitioners, patients, policy makers) representing 14 countries reviewed evidence summaries and rated the importance of 80 criteria in 12 quality domains on a 1 to 9 scale. Second round participants received feedback from the first round and repeated their assessment of the 80 criteria plus three new ones. MAIN OUTCOME MEASURE: Aggregate ratings for each criterion calculated using medians weighted to compensate for different numbers in stakeholder groups; criteria rated between 7 and 9 were retained. RESULTS: 212 nominated people were invited to participate. Of those invited, 122 participated in the first round (77 researchers, 21 patients, 10 practitioners, 14 policy makers); 104/122 (85%) participated in the second round. 74 of 83 criteria were retained in the following domains: systematic development process (9/9 criteria); providing information about options (13/13); presenting probabilities (11/13); clarifying and expressing values (3/3); using patient stories (2/5); guiding/coaching (3/5); disclosing conflicts of interest (5/5); providing internet access (6/6); balanced presentation of options (3/3); using plain language (4/6); basing information on up to date evidence (7/7); and establishing effectiveness (8/8). CONCLUSIONS: Criteria were given the highest ratings where evidence existed, and these were retained. Gaps in research were highlighted. Developers, users, and purchasers of patient decision aids now have a checklist for appraising quality. An instrument for measuring quality of decision aids is being developed.

Decision Support Techniques↗
PubMed2006

Doing the right thing: systems support for decision quality in cancer care.

BACKGROUND: There is considerable evidence of problems with the quality of cancer care. Wide variation in rates of interventions suggests that cancer care decisions may not reflect the preferences of informed patients. PURPOSE: To present a framework for systems support for improving the quality of decisions in cancer. METHODS: We outlined the types of decisions faced by cancer patients and categorized them based on the level of evidence available about effectiveness of choices and the amount of variation in patients' preferences for the key outcomes. Then we describe appropriate strategies to systematically improve the quality of decision making for each category. RESULTS: The types of decisions faced by cancer patients and providers are varied. The appropriate strategy to drive improvements differs for different decisions. For complex, preference-sensitive decisions, improvements in decision quality require increasing patients' knowledge and the match between patients' preferences and treatments. CONCLUSIONS: Decision making in cancer care is complex. Neither patients nor providers can make treatment decisions alone. System support is needed to improve the quality of decisions.

Choice Behavior↗
PubMed2006

Understanding treatment decision making: contexts, commonalities, complexities, and challenges.

BACKGROUND: The diagnosis of cancer sets off a cascade of complex decisions at a time when patients feel vulnerable and distressed. Although clinical decisions used to follow one standard, many guidelines now outline several options and include explicit recognition of the need to incorporate patients' preferences to determine the most appropriate treatment. PURPOSE: The purpose of this article is to provide a brief overview of empirical studies about cancer patients' treatment-related decision making, to highlight the areas of congruence and divergence in that empirical literature, and then to generate a framework that points to future interventions and research. METHODS: Through a group discussion with a range of experts in the field, we generated a framework for the critical treatment decisions and key issues within those decisions. Then, we reviewed the literature describing the experiences of cancer patients and evaluating interventions designed to improve the quality of treatment decisions. RESULTS: We identified four major differences that influence decision making across cancers and across individuals with the same diagnosis. We also identified four common themes across situations and people. There is considerable evidence that decision aids can improve the quality of decisions across a range of diseases, although the data for cancer treatment decision making are limited. Other interventions such as navigation-skill training are promising but have little evidence of benefit for cancer decisions. CONCLUSIONS: There are many opportunities for behavioral research to extend and contribute to the understanding and improvement of cancer treatment decision making. Some key areas in need of research include developing taxonomies of disease and patient characteristics and increasing understanding of the lived experiences of cancer survivors, of the influence of time and timing, of the relationship of information and preferences, and of participation in randomized clinical trials.

Comprehension↗
PubMed2004

Applying the neoadjuvant paradigm to ductal carcinoma in situ.

Local treatment options for ductal carcinoma in situ (DCIS) are virtually identical to those for early invasive breast cancer, despite the fact that the survival from this condition is much higher. Our ability to more appropriately tailor therapy for DCIS is hampered by a lack of understanding of the natural history of DCIS, our limited ability to predict the rate of progression to invasive cancer and the response to therapy, and the absence of tools to follow patients who have not had invasive treatments. Neoadjuvant therapy, which has been proven to be both safe and effective in tailoring treatments for invasive cancer, could be ideally suited to DCIS. However, neoadjuvant therapy requires that doctors and patients delay surgical treatment that has known benefits. In order to successfully introduce this approach into clinical practice, risk assessment and decision support tools will be needed to help physicians and patients feel comfortable that they are not being exposed to unnecessary or excessive risk. In addition, we need better imaging to track extent and progression of disease. Among the possible benefits of the neoadjuvant approach, we may discover that many lesions are responsive and some even reversible, leaving us with treatments that might be tailored to biology and with important clues for breast cancer prevention in high-risk women.

Breast Neoplasms↗