Search

BackgroundMost 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. ObjectiveThe 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. MethodsThe 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. ResultsPrimary 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. ConclusionsThis 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 RegistrationClinicalTrials.gov NCT01963234; https://clinicaltrials.gov/ct2/show/NCT01963234 (Archived by WebCite at http://www.webcitation.org/78qXQJvVI)

Context: Primary care clinics Objective: As part of an effort to design an implementation strategy tailoring tool, our research group sought to understand what is known about how contextual factors and prescriber characteristics affect the adoption of guideline-concordant opioid-prescribing practices in primary care settings. Study Design and Analysis: We conducted a realist synthesis of 71 articles. Setting or Dataset: Global literature review Population Studied: Patients on long-term opioid therapy for chronic pain Intervention/Instrument: N/A Outcome Measures: Adoption of clinical guidelines for opioid prescribing in primary care settings. Results: We found that adoption is related to contextual factors at the individual, clinic, health system and environmental levels, which operate via intrapersonal, interpersonal, organisational and structural mechanisms. Prescriber level factors tending towards adherence included: training in palliative care, pain management, mental health, or addiction medicine; frequent, high-volume opioid prescribing; medication for opioid use disorder prescribing; and knowing someone who has died of an opioid overdose. Prescriber level factors tending towards non-adherence included low-volume, low-dose prescribing; pharmaceutical company-sponsored training in opioids; and incentives from pharmaceutical companies. Clinic level factors tending towards adherence included a team- based culture; interdisciplinarity; and a culture of QI. Clinic level factors tending towards non-adherence included turf battles and clinician conflict about opioid prescribing. Health system factors tending towards adherence included clear policies toward opioid prescribing, including mandates; and resources devoted to supporting guideline adherence. Health system factors tending towards non- adherence included high demand/low support; workflows that do not accommodate guideline concordant care; administrative intrusion into clinical decision-making; and inadequate prescriber compensation. Environmental factors tending towards non-adherence included poverty and health disparities. Conclusion: A static model cannot capture the complexity of the relationships between context, mechanisms and outcomes regarding adherence to opioid prescribing guidelines. Instead, deeper understanding requires dynamic models that conceptualize clusters of contextual factors and mechanisms that tend toward guideline concordance and clusters that tend toward non-concordance., Presenters Andrew Quanbeck, [...]

The advent of mobile-phone technology has been one of the most influential technological advances in world history. In 2014, the International Telecommunications Union (ITU) estimated that the number of mobile-phone [...]

Background and Aims: Management of alcohol use disorder (AUD) could be enhanced by effective remote treatments. This study tested whether supplementing intensive outpatient programs (IOPs) with continuing care delivered via (1) telephone, (2) smartphone or (3) their combination improves outcomes relative to (4) IOP only. Continuing care conditions were also compared. Design Randomized controlled trial of four groups with 3‐, 6‐, 9‐, 12‐ and 18‐month follow‐ups. Setting: University research center in Philadelphia, PA, USA. Participants: Participants (n = 262) met DSM‐V criteria for AUD, were largely male (71%) and African American (82%). Interventions and Comparator: Telephone monitoring and counseling (TMC; n = 59), addiction comprehensive health enhancement support system (ACHESS; n = 68) and TMC + ACHESS (n = 70) provided for 12 months. The control condition received IOP only (TAU; n = 65). Measurement The primary outcome was percentage of days heavy drinking (PDHD) in months 1–12. Secondary outcomes were any drinking, any drug use, drinking consequences and quality of life. Findings Mean PDHD in months 1–12 was 10.29 in TAU, 5.41 in TMC, 6.80 in ACHESS and 5.99 in TMC + ACHESS. PDHD was lower in TMC [Cohen's d = 0.35, P = 0.018, 95% confidence interval (CI) = (−1.42, −0.20)], ACHESS [d = 0.31, P = 0.031, 95% CI = (−1.27, −0.06)] and TMC + ACHESS [d = 0.36, P = 0.009, 95% CI = (−1.40, −0.20)] than in TAU. Differences between TMC + ACHESS, TMC and ACHESS were small (d ≤ 0.06) and non‐significant. Findings were inconclusive as to whether or not the treatment conditions differed on PDHD at 18 months. A significant effect was obtained on any drinking, which was higher in months 1–12 in TAU than in TMC [odds ratio (OR) = 3.02, standard error (SE) = 0.43, 95% CI = (1.30, 6.99), P = 0.01] and TMC + ACHESS [OR = 2.43, SE = 0.39, 95% CI = (1.12, 5.27), P = 0.025). No other significant effects were obtained on other secondary outcomes during or after treatment. Conclusions: A telephone‐delivered intervention and a smartphone‐delivered intervention, alone and in combination, provided effective remote continuing care for alcohol use disorder. The combination of both interventions was not superior to either alone and effects did not persist post‐treatment. [ABSTRACT FROM AUTHOR]

Abstract Background Dissemination is a critical facet of implementing quality improvement in organizations. As a field, addiction treatment has produced effective interventions but disseminated them slowly and reached only a fraction of people needing treatment. This study investigates four methods of disseminating quality improvement (QI) to addiction treatment programs in the U.S. It is, to our knowledge, the largest study of organizational change ever conducted in healthcare. The trial seeks to determine the most cost-effective method of disseminating quality improvement in addiction treatment. Methods The study is evaluating the costs and effectiveness of different QI approaches by randomizing 201 addiction-treatment programs to four interventions. Each intervention used a web-based learning kit plus monthly phone calls, coaching, face-to-face meetings, or the combination of all three. Effectiveness is defined as reducing waiting time (days between first contact and treatment), increasing program admissions, and increasing continuation in treatment. Opportunity costs will be estimated for the resources associated with providing the services. Outcomes The study has three primary outcomes: waiting time, annual program admissions, and continuation in treatment. Secondary outcomes include: voluntary employee turnover, treatment completion, and operating margin. We are also seeking to understand the role of mediators, moderators, and other factors related to an organization's success in making changes. Analysis We are fitting a mixed-effect regression model to each program's average monthly waiting time and continuation rates (based on aggregated client records), including terms to isolate state and intervention effects. Admissions to treatment are aggregated to a yearly level to compensate for seasonality. We will order the interventions by cost to compare them pair-wise to the lowest cost intervention (monthly phone calls). All randomized sites with outcome data will be included in the analysis, following the intent-to-treat principle. Organizational covariates in the analysis include program size, management score, and state. Discussion The study offers seven recommendations for conducting a large-scale cluster-randomized trial: provide valuable services, have aims that are clear and important, seek powerful allies, understand the recruiting challenge, cultivate commitment, address turnover, and encourage rigor and flexibility. Trial Registration ClinicalTrials. govNCT00934141

Health care transformation calls for patient engagement in quality improvement (PEQI), yet practice participation remains low. This pilot study of 8 primary care clinics at 7 statewide locations sought to determine the most effective strategies for disseminating a previously successful single-system PEQI intervention. Qualitative data were obtained through site visits, interviews, observations, and journaling. All material pertaining to barriers, recruitment/retention, and implementation was extracted, compared, and categorized. Five teams partially completed the intervention and 3 finished. These 3 teams did not ask for shorter trainings and were assigned a quality improvement (QI) coach. Multiple barriers to recruitment, implementation, and retention were noted at the organizational and clinic/team level, including turnover, shifting priorities, cross-level communication difficulties, lack of QI knowledge, and confusion between patient engagement and patient activation. These findings suggest that QI facilitation and dedicated time can help primary care teams identify and overcome barriers to PEQI. [ABSTRACT FROM AUTHOR]