Dataset Information

Effects of Shared Decision Making on Opioid Prescribing After Hysterectomy.

ABSTRACT: OBJECTIVE:To evaluate the effects of shared decision making using a simple decision aid for opioid prescribing after hysterectomy. METHODS:We conducted a prospective quality initiative study including all patients undergoing hysterectomy for benign, nonobstetric indications between March 1, 2018, and July 31, 2018, at our academic institution. Using a visual decision aid, patients received uniform education regarding postoperative pain management. They were then educated on the department's guidelines regarding the maximum number of tablets recommended per prescription and the mean number of opioid tablets used by a similar cohort of patients in a previously published study at our institution. Patients were then asked to choose their desired number of tablets to receive on discharge. Structured telephone interviews were conducted 14 days after surgery. The primary outcome was total opioids prescribed before compared with after implementation of the decision aid. Secondary outcomes included opioid consumption, patient satisfaction, and refill requests after intervention implementation. RESULTS:Of 170 eligible patients, 159 (93.5%) used the decision aid (one patient who used the decision aid was subsequently excluded from the analysis owing to significant perioperative complications), including 110 (69.6%) laparoscopic, 40 (25.3%) vaginal, and eight (5.3%) abdominal hysterectomies. Telephone surveys were completed for 89.2% (n=141) of participants. Student's t-test showed that patients who participated in the decision aid (post-decision aid cohort) were discharged with significantly fewer oral morphine equivalents than patients who underwent hysterectomy before implementation of the decision aid (pre-decision aid cohort) (92±35 vs 160±81, P<.01), with no significant change in the number of requested refills (9.5% [n=15] vs 5.7% [n=14], P=.15). In the post-decision aid cohort, 76.6% of patients (n=121) chose fewer tablets than the guideline-allotted maximum. Approximately 76% of patients (n=102) reported having leftover tablets. CONCLUSION:This quality improvement initiative illustrates that a simple decision aid can result in a significant decrease in opioid prescribing without compromising patient satisfaction or postoperative pain management.

SUBMITTER: Vilkins AL

PROVIDER: S-EPMC6945818 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Effects of Shared Decision Making on Opioid Prescribing After Hysterectomy.

Vilkins Annmarie L AL Sahara Michael M Till Sara R SR Ceci Christina C Howard Ryan R Griffith Kendall C KC Waljee Jennifer F JF Lim Courtney S CS Skinner Bethany D BD Clauw Daniel J DJ Brummett Chad M CM As-Sanie Sawsan S

Obstetrics and gynecology 20191001 4

<h4>Objective</h4>To evaluate the effects of shared decision making using a simple decision aid for opioid prescribing after hysterectomy.<h4>Methods</h4>We conducted a prospective quality initiative study including all patients undergoing hysterectomy for benign, nonobstetric indications between March 1, 2018, and July 31, 2018, at our academic institution. Using a visual decision aid, patients received uniform education regarding postoperative pain management. They were then educated on the de ...[more]

PMID: 31503160

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Project description:PurposeWe examined whether an educational, shared-decision-making tool designed to empower patients, individualize pain management, and maximize use of nonopioid, over-the-counter analgesics reduces opioid use and waste while maintaining adequate pain relief.MethodsWe developed an educational, shared-decision-making tool regarding postoperative pain medication for outpatient hand surgery. Patients randomized to groups with and without the tool were surveyed for 4 weeks after surgery. Survey variables included Patient-Reported Outcomes Measurement Information System pain intensity and pain interference scores, as well as the number of oxycodone or over-the-counter pills taken. Results were compared using chi-squared, Wilcoxon rank-sum, and Welch's t tests.ResultsFifty-three patients participated: 25 in the shared-tool group and 28 in the no-tool group. The mean age was 60 years, with more women in the no-tool group than the shared-tool group (n =17 versus 11, respectively). The shared-tool group averaged 6.4 prescribed oxycodone pills, versus 10 for the no-tool group (P < .01). The median numbers of oxycodone pills taken the first week after surgery were 2 (interquartile range, 6) for the shared-tool group and 3 (interquartile range, 6) for the no-tool group (P = .97). Patient-reported outcome measures for pain intensity and pain interference were not significantly different for weeks 1, 3, and 4 after surgery. Pain interference was significantly lower in week 2 in the shared-tool group (difference, -4.4; 95% confidence interval, -8.57 to -0.30; P = .04).ConclusionsThe shared-tool group had equivalent or better pain control and were prescribed a lower number of opioid pain pills than the no-tool group. Both groups used nonopioid medications, with no difference in the types of over-the-counter medications used. Shared decision-making strategies could be applied to other outpatient orthopedic surgical settings, and may reduce the amount of opioids prescribed without compromising pain control.Type of study/level of evidenceTherapeutic II.

Project description:BackgroundShared decision-making (SDM) is widely recommended and required by the Centers for Medicare and Medicaid for patients considering lung cancer screening (LCS).ObjectiveWe examined clinicians' communication practices and perceived barriers of SDM for LCS at three medical centers with established screening programs.DesignMulticenter qualitative study of clinicians participating in LCS.ApproachWe performed semi-structured interviews, which were transcribed and analyzed using directed content analysis, guided by a theoretical model of patient-clinician communication.ParticipantsWe interviewed 24 clinicians including LCS coordinators (2), pulmonologists (3), and primary care providers (17), 4 of whom worked for the LCS program, a thoracic surgeon, and a radiologist.ResultsAll clinicians agreed with the goal of SDM, to ensure the screening decision was congruent with the patient's values. The depth and type of information presented by each clinician role varied considerably. LCS coordinators presented detailed information including numeric estimates of benefit and harm. Most PCPs explained the process more generally, focusing on logistics and the high rate of nodule detection. No clinician explicitly elicited values or communication preferences. Many PCPs tailored the conversation based on their implicit understanding of patients' values and preferences, gained from past experiences. PCPs reported that time, lack of detailed personal knowledge of LCS, and patient preferences were barriers to SDM. Many clinicians perceived that a significant proportion of patients were not interested in specific percentages and preferred to receive a clinician recommendation.ConclusionsOur results suggest that clinicians support the goal of SDM for LCS decisions but PCPs may not perform some of its elements. The lack of completion of some elements, such as PCPs' lack of in-depth information exchange, may reflect perceived patient preferences for communication. As LCS is implemented, further research is needed to support a personalized, patient-centered approach to produce better outcomes.

Project description:Background. Accurate diagnosis of patients' preferences is central to shared decision making. Missing from clinical practice is an approach that links pretreatment preferences and patient-reported outcomes. Objective. We propose a Bayesian collaborative filtering (CF) algorithm that combines pretreatment preferences and patient-reported outcomes to provide treatment recommendations. Design. We present the methodological details of a Bayesian CF algorithm designed to accomplish 3 tasks: 1) eliciting patient preferences using conjoint analysis surveys, 2) clustering patients into preference phenotypes, and 3) making treatment recommendations based on the posttreatment satisfaction of like-minded patients. We conduct a series of simulation studies to test the algorithm and to compare it to a 2-stage approach. Results. The Bayesian CF algorithm and 2-stage approaches performed similarly when there was extensive overlap between preference phenotypes. When the treatment was moderately associated with satisfaction, both methods made accurate recommendations. The kappa estimates measuring agreement between the true and predicted recommendations were 0.70 (95% confidence interval = 0.052-0.88) and 0.73 (0.56-0.90) under the Bayesian CF and 2-stage approaches, respectively. The 2-stage approach failed to converge in settings in which clusters were well separated, whereas the Bayesian CF algorithm produced acceptable results, with kappas of 0.73 (0.56-0.90) and 0.83 (0.69-0.97) for scenarios with moderate and large treatment effects, respectively. Limitations. Our approach assumes that the patient population is composed of distinct preference phenotypes, there is association between treatment and outcomes, and treatment effects vary across phenotypes. Findings are also limited to simulated data. Conclusion. The Bayesian CF algorithm is feasible, provides accurate cluster treatment recommendations, and outperforms 2-stage estimation when clusters are well separated. As such, the approach serves as a roadmap for incorporating predictive analytics into shared decision making.

Dataset Information

Effects of Shared Decision Making on Opioid Prescribing After Hysterectomy.

Publications

Effects of Shared Decision Making on Opioid Prescribing After Hysterectomy.

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