Oncologist use of the Adjuvant! model for risk communication: a pilot study examining patient knowledge of 10-year prognosis

<p>Abstract</p> <p>Background</p> <p>Our purpose was to collect preliminary data on newly diagnosed breast cancer patient knowledge of prognosis before and after oncology visits. Many oncologists use a validated prognostic software model, Adjuvant!, to estimate 10-year recurrence and mortality outcomes for breast cancer local and adjuvant therapy. Some oncologists are printing Adjuvant! screens to use as visual aids during consultations. No study has reported how such use of Adjuvant! printouts affects patient knowledge of prognosis. We hypothesized that Adjuvant! printouts would be associated with significant changes in the proportion of patients with accurate understanding of local therapy prognosis.</p> <p>Methods</p> <p>We recruited a convenience sample of 20 patients seen by 2 senior oncologists using Adjuvant! printouts of recurrence and mortality screens in our academic medical center. We asked patients for their estimates of local therapy recurrence and mortality risks and counted the number of patients whose estimates were within ± 5% of Adjuvant! before and after the oncology visit, testing whether pre/post changes were significant using McNemar's two-sided test at a significance level of 5%.</p> <p>Results</p> <p>Two patients (10%) accurately estimated local therapy recurrence and mortality risks before the oncology visit, while seven out of twenty (35%) were accurate afterwards (p = 0.125).</p> <p>Conclusion</p> <p>A majority of patients in our sample were inaccurate in estimating their local therapy recurrence and mortality risks, even after being shown printouts summarizing these risks during their oncology visits. Larger studies are needed to replicate or repudiate these preliminary findings, and test alternative methods of presenting risk estimates. Meanwhile, oncologists should be wary of relying exclusively on Adjuvant! printouts to communicate local therapy recurrence and mortality estimates to patients, as they may leave a majority of patients misinformed.</p

Measuring decision quality: psychometric evaluation of a new instrument for breast cancer chemotherapy

Abstract Background Women diagnosed with early stage (I or II) breast cancer face a highly challenging decision – whether or not to undergo adjuvant chemotherapy. We developed a decision quality instrument for chemotherapy for early stage breast cancer and sought to evaluate its performance. Methods Cross-sectional, mailed survey of recent breast cancer survivors, providers, and healthy controls and a retest survey of survivors. The decision quality instrument includes questions on knowledge and personal goals. It results in a knowledge score and concordance score, which reflects the percentage of patients who received treatments that match their goals. Hypotheses related to acceptability, feasibility, validity, and reliability of the survey instrument were examined. Results Responses were received from 352 patients, 89 providers and 35 healthy controls. The decision quality instrument was feasible to implement with few missing data. The knowledge scores had good retest reliability (intraclass correlation coefficient (ICC) =0.75). Knowledge scores discriminated between providers and patients (mean difference 31.1%, 95% CI 26.9, 35.3) and between patients and healthy controls (mean difference 11.2, 95% CI 5.4, 17.1). Most providers reported that the knowledge items covered essential content. Two of the five goal items had a ceiling effect, and one goal had low content validity. The goal items had moderate retest reliability (ICC’s 0.57 to 0.78). In the multivariable model of treatment, none of the patient goals was associated with receipt of chemotherapy. Age and hormone receptor status were the only variables independently associated with chemotherapy. Most patients (77.6%) had treatment concordant with that predicted by the model. Patients who had concordant treatment had similar levels of confidence and regret as those who did not. Conclusions The Decision Quality Instrument is a reliable and valid measure of patient knowledge about chemotherapy, but its ability to measure concordance with patient goals is limited. In this sample, patient goals were not associated with treatment, and most patients reported they were not asked their preference, suggesting that goals were not adequately considered in decision making

Sustainment of the TeleSleep program for rural veterans

BackgroundIn fiscal year 2021, the Veterans Health Administration (VHA) provided care for sleep disorders to 599,966 Veterans, including 189,932 rural Veterans. To further improve rural access, the VA Office of Rural Health developed the TeleSleep Enterprise-Wide Initiative (EWI). TeleSleep's telemedicine strategies include tests for sleep apnea at the Veteran's home rather than in a sleep lab; Clinical Video Telehealth applications; and other forms of virtual care. In 2017 and 2020, VHA provided 3-year start-up funding to launch new TeleSleep programs at rural-serving VA medical facilities.MethodsIn early 2022, we surveyed leaders of 24 sites that received TeleSleep funding to identify successes, failures, facilitators, and barriers relevant to sustaining TeleSleep implementations upon expiration of startup funding. We tabulated frequencies on the multiple choice questions in the survey, and, using the survey's critical incident framework, summarized the responses to open-ended questions. TeleSleep program leaders discussed the responses and synthesized recommendations for improvement.Results18 sites reported sustainment, while six were “on track.” Sustainment involved medical centers or regional entities incorporating TeleSleep into their budgets. Facilitators included: demonstrating value; aligning with local priorities; and collaborating with spoke sites serving rural Veterans. Barriers included: misalignment with local priorities; and hiring delays. COVID was a facilitator, as it stimulated adoption of telehealth practices; and also a barrier, as it consumed attention and resources. Recommendations included: longer startup funding; dedicated funding for human resources to accelerate hiring; funders communicating with local facility leaders regarding how TeleSleep aligns with organizational priorities; hiring into job classifications aligned with market pay; and obtaining, from finance departments, projections and outcomes for the return on investment in TeleSleep