Curriculum Changes and Trends 2010-2020: A Focused National Review Using the AAMC Curriculum Inventory and the LCME Annual Medical School Questionnaire Part II.

Medical school curricula have evolved from 2010 to 2020. Numerous pressures and influences affect medical school curricula, including those from external sources, academic medical institutions, clinical teaching faculty, and undergraduate medical students. Using data from the AAMC Curriculum Inventory and the LCME Annual Medical School Questionnaire Part II, the nature of curriculum change is illuminated. Most medical schools are undertaking curriculum change, both in small cycles of continuous quality improvement and through significant change to curricular structure and content. Four topic areas are explored: cost consciousness, guns and firearms, nutrition, and opioids and addiction medicine. The authors examine how these topic areas are taught and assessed, where in the curriculum they are located, and how much time is dedicated to them in relation to the curriculum as a whole. When examining instructional methods overall, notable findings include (1) the decrease of lecture, although lecture remains the most used instructional method, (2) the increase of collaborative instructional methods, (3) the decrease of laboratory, and (4) the prevalence of clinical instructional methods in academic levels 3 and 4. Regarding assessment methods overall, notable findings include (1) the recent change of the USMLE Step 1 examination to a pass/fail reporting system, (2) a modest increase in narrative assessment, (3) the decline of practical labs, and (4) the predominance of institutionally developed written/computer-based examinations and participation. Among instructional and assessment methods, the most used methods tend to cluster by academic level. It is critical that faculty development evolves alongside curricula. Continued diversity in the use of instructional and assessment methods is necessary to adequately prepare tomorrow's physicians. Future research into the life cycle of a curriculum, as well optional curriculum content, is warranted.

Using Stakeholder Values to Promote Implementation of an Evidence-Based Mobile Health Intervention for Addiction Treatment in Primary Care Settings.

BACKGROUND: Most evidence-based practices (EBPs) do not find their way into clinical use, including evidence-based mobile health (mHealth) technologies. The literature offers implementers little practical guidance for successfully integrating mHealth into health care systems. OBJECTIVE: The goal of this research was to describe a novel decision-framing model that gives implementers a method of eliciting the considerations of different stakeholder groups when they decide whether to implement an EBP. METHODS: The decision-framing model can be generally applied to EBPs, but was applied in this case to an mHealth system (Seva) for patients with addiction. The model builds from key insights in behavioral economics and game theory. The model systematically identifies, using an inductive process, the perceived gains and losses of different stakeholder groups when they consider adopting a new intervention. The model was constructed retrospectively in a parent implementation research trial that introduced Seva to 268 patients in 3 US primary care clinics. Individual and group interviews were conducted to elicit stakeholder considerations from 6 clinic managers, 17 clinicians, and 6 patients who were involved in implementing Seva. Considerations were used to construct decision frames that trade off the perceived value of adopting Seva versus maintaining the status quo from each stakeholder group's perspective. The face validity of the decision-framing model was assessed by soliciting feedback from the stakeholders whose input was used to build it. RESULTS: Primary considerations related to implementing Seva were identified for each stakeholder group. Clinic managers perceived the greatest potential gain to be better care for patients and the greatest potential loss to be cost (ie, staff time, sustainability, and opportunity cost to implement Seva). All clinical staff considered time their foremost consideration-primarily in negative terms (eg, cognitive burden associated with learning a new system) but potentially in positive terms (eg, if Seva could automate functions done manually). Patients considered safety (anonymity, privacy, and coming from a trusted source) to be paramount. Though payers were not interviewed directly, clinic managers judged cost to be most important to payers-whether Seva could reduce total care costs or had reimbursement mechanisms available. This model will be tested prospectively in a forthcoming mHealth implementation trial for its ability to predict mHealth adoption. Overall, the results suggest that implementers proactively address the cost and burden of implementation and seek to promote long-term sustainability. CONCLUSIONS: This paper presents a model implementers may use to elicit stakeholders' considerations when deciding to adopt a new technology, considerations that may then be used to adapt the intervention and tailor implementation, potentially increasing the likelihood of implementation success. TRIAL REGISTRATION: ClinicalTrials.gov NCT01963234; https://clinicaltrials.gov/ct2/show/NCT01963234 (Archived by WebCite at http://www.webcitation.org/78qXQJvVI).

Buprenorphine unobserved "home" induction: a survey of Ontario's addiction physicians.

BACKGROUND: Ontario patients on opioid agonist treatment (OAT) are often prescribed methadone instead of buprenorphine, despite the latter's superior safety profile. Ontario OAT providers were surveyed to better understand their attitudes towards buprenorphine and potential barriers to its use, including the induction process. METHODS: We used a convenience sample from an annual provincial conference to which Ontario physicians who are involved with OAT are invited. RESULTS: Based on 85 survey respondents (out of 215 attendees), only 4% of Ontario addiction physicians involved in OAT routinely used unobserved "home" buprenorphine induction: 59% of physicians felt that unobserved induction was risky because it was against "the guidelines" and 66% and 61% respectively believed that unobserved "home" induction increased the risk of diversion and of precipitated withdrawal. CONCLUSIONS: Ontario addiction physicians largely report following the traditional method of bringing in patients for observed in-office buprenorphine induction: they expressed fear of precipitated withdrawal, diversion, and going against clinical guidelines. The hesitance in using unobserved induction may explain, in part, Ontario's reliance on methadone.

Assessment of Addiction Medicine Training in Family Medicine Residency Programs: A CERA Study.

BACKGROUND AND OBJECTIVES: Substance use disorder (SUD) is a widespread problem but physicians may feel inadequately prepared to provide addiction care. We sought to assess current addiction medicine curricula in US family medicine residencies (FMRs) and evaluate barriers to improving or implementing addiction medicine curricula. METHODS: Questions regarding addiction medicine training were added to the December 2015 Council of Academic Family Medicine Educational Research Alliance (CERA) survey to US FMR program directors to evaluate each FMR's curriculum, potential workforce production, perceived barriers to improving or implementing curricula and faculty training in addiction medicine. RESULTS: Of 461 FMR directors, 227 (49.2%) responded; 28.6% reported a required addiction medicine curricula. Regional variations of having a required curriculum ranged from 41.3% in the Northeast to 20.0% in the South (P=0.07). Of residencies, 31.2% had at least one graduate obtain a buprenorphine prescription waiver in the past year and 8.6% had at least one graduate pursue an addiction medicine fellowship in the past 5 years. Lack of faculty expertise was the most commonly cited barrier to having a curriculum, with only 36.2% of programs having at least one buprenorphine waivered faculty member, 9.4% an addiction medicine board certified faculty, and 5.5% a fellowship trained faculty. CONCLUSIONS: Few FMRs have addiction medicine curricula and most graduates do not seek additional training. Multifaceted efforts, including developing model national curricula, training existing faculty, and recruiting addiction trained faculty, may improve addiction medicine training in family medicine residencies to better address the growing SUD epidemic.

Assessing Differences in the Availability of Opioid Addiction Therapy Options: Rural Versus Urban and American Indian Reservation Versus Nonreservation.

BACKGROUND: Opioid misuse is a large public health problem in the United States. Residents of rural areas and American Indian (AI) reservation/trust lands represent traditionally underserved populations with regard to substance-use disorder therapy. PURPOSE: Assess differences in the number of opioid agonist therapy (OAT) facilities and physicians with Drug Addiction Treatment Act (DATA) waivers for rural versus urban, and AI reservation/trust land versus non-AI reservation/trust land areas in Washington State. METHODS: The unit of analysis was the ZIP code. The dependent variables were the number of OAT facilities and DATA-waivered physicians in a region per 10,000 residents aged 18-64 in a ZIP code. A region was defined as a ZIP code and its contiguous ZIP codes. The independent variables were binary measures of whether a ZIP code was classified as rural versus urban, or AI reservation/trust land versus non-AI reservation/trust land. Zero-inflated negative binomial regressions with robust standard errors were estimated. RESULTS: The number of OAT clinics in a region per 10,000 ZIP-code residents was significantly lower in rural versus urban areas (P = .002). This did not differ significantly between AI reservation/trust land and non-AI reservation/trust land areas (P = .79). DATA-waivered physicians in a region per 10,000 ZIP-code residents was not significantly different between rural and urban (P = .08), or AI reservation/trust land versus non-AI reservation/trust land areas (P = .21). CONCLUSIONS: It appears that the potential for Washington State residents of rural and AI reservation areas to receive OAT is similar to that of residents outside of those areas; however, difficulties in accessing therapy may remain, highlighting the importance of expanding health care insurance and providing support for DATA-waivered physicians.

Using Bayes factors for testing hypotheses about intervention effectiveness in addictions research.

BACKGROUND AND AIMS: It has been proposed that more use should be made of Bayes factors in hypothesis testing in addiction research. Bayes factors are the ratios of the likelihood of a specified hypothesis (e.g. an intervention effect within a given range) to another hypothesis (e.g. no effect). They are particularly important for differentiating lack of strong evidence for an effect and evidence for lack of an effect. This paper reviewed randomized trials reported in Addiction between January and June 2013 to assess how far Bayes factors might improve the interpretation of the data. METHODS: Seventy-five effect sizes and their standard errors were extracted from 12 trials. Seventy-three per cent (n = 55) of these were non-significant (i.e. P > 0.05). For each non-significant finding a Bayes factor was calculated using a population effect derived from previous research. In sensitivity analyses, a further two Bayes factors were calculated assuming clinically meaningful and plausible ranges around this population effect. RESULTS: Twenty per cent (n = 11) of the non-significant Bayes factors were < â…“ and 3.6% (n = 2) were > 3. The other 76.4% (n = 42) of Bayes factors were between ⅓ and 3. Of these, 26 were in the direction of there being an effect (Bayes factor > 1 and < 3); 12 tended to favour the hypothesis of no effect (Bayes factor < 1 and > â…“); and for four there was no evidence either way (Bayes factor = 1). In sensitivity analyses, 13.3% of Bayes Factors were < â…“ (n = 20), 62.7% (n = 94) were between ⅓ and 3 and 24.0% (n = 36) were > 3, showing good concordance with the main results. CONCLUSIONS: Use of Bayes factors when analysing data from randomized trials of interventions in addiction research can provide important information that would lead to more precise conclusions than are obtained typically using currently prevailing methods.