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Integrating addiction treatment into primary care using mobile health technology: protocol for an implementation research study.

ABSTRACT: BACKGROUND:Healthcare reform in the United States is encouraging Federally Qualified Health Centers and other primary-care practices to integrate treatment for addiction and other behavioral health conditions into their practices. The potential of mobile health technologies to manage addiction and comorbidities such as HIV in these settings is substantial but largely untested. This paper describes a protocol to evaluate the implementation of an E-Health integrated communication technology delivered via mobile phones, called Seva, into primary-care settings. Seva is an evidence-based system of addiction treatment and recovery support for patients and real-time caseload monitoring for clinicians. METHODS/DESIGN:Our implementation strategy uses three models of organizational change: the Program Planning Model to promote acceptance and sustainability, the NIATx quality improvement model to create a welcoming environment for change, and Rogers's diffusion of innovations research, which facilitates adaptations of innovations to maximize their adoption potential. We will implement Seva and conduct an intensive, mixed-methods assessment at three diverse Federally Qualified Healthcare Centers in the United States. Our non-concurrent multiple-baseline design includes three periods - pretest (ending in four months of implementation preparation), active Seva implementation, and maintenance - with implementation staggered at six-month intervals across sites. The first site will serve as a pilot clinic. We will track the timing of intervention elements and assess study outcomes within each dimension of the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework, including effects on clinicians, patients, and practices. Our mixed-methods approach will include quantitative (e.g., interrupted time-series analysis of treatment attendance, with clinics as the unit of analysis) and qualitative (e.g., staff interviews regarding adaptations to implementation protocol) methods, and assessment of implementation costs. DISCUSSION:If implementation is successful, the field will have a proven technology that helps Federally Qualified Health Centers and affiliated organizations provide addiction treatment and recovery support, as well as a proven strategy for implementing the technology. Seva also has the potential to improve core elements of addiction treatment, such as referral and treatment processes. A mobile technology for addiction treatment and accompanying implementation model could provide a cost-effective means to improve the lives of patients with drug and alcohol problems. TRIAL REGISTRATION:ClinicalTrials.gov (NCT01963234).

SUBMITTER: Quanbeck AR

PROVIDER: S-EPMC4072605 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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Integrating addiction treatment into primary care using mobile health technology: protocol for an implementation research study.

Quanbeck Andrew R AR Gustafson David H DH Marsch Lisa A LA McTavish Fiona F Brown Randall T RT Mares Marie-Louise ML Johnson Roberta R Glass Joseph E JE Atwood Amy K AK McDowell Helene H

Implementation science : IS 20140529

<h4>Background</h4>Healthcare reform in the United States is encouraging Federally Qualified Health Centers and other primary-care practices to integrate treatment for addiction and other behavioral health conditions into their practices. The potential of mobile health technologies to manage addiction and comorbidities such as HIV in these settings is substantial but largely untested. This paper describes a protocol to evaluate the implementation of an E-Health integrated communication technolog ...[more]

PMID: 24884976

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Project description:BACKGROUND:Despite the near ubiquity of mobile phones, little research has been conducted on the implementation of mobile health (mHealth) apps to treat patients in primary care. Although primary care clinicians routinely treat chronic conditions such as asthma and diabetes, they rarely treat addiction, a common chronic condition. Instead, addiction is most often treated in the US health care system, if it is treated at all, in a separate behavioral health system. mHealth could help integrate addiction treatment in primary care. OBJECTIVE:The objective of this paper was to report the effects of implementing an mHealth system for addiction in primary care on both patients and clinicians. METHODS:In this implementation research trial, an evidence-based mHealth system named Seva was introduced sequentially over 36 months to a maximum of 100 patients with substance use disorders (SUDs) in each of three federally qualified health centers (FQHCs; primary care clinics that serve patients regardless of their ability to pay). This paper reports on patient and clinician outcomes organized according to the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. RESULTS:The outcomes according to the RE-AIM framework are as follows: Reach-Seva reached 8.31% (268/3226) of appropriate patients. Reach was limited by our ability to pay for phones and data plans for a maximum of 100 patients per clinic. Effectiveness-Patients who were given Seva had significant improvements in their risky drinking days (44% reduction, (0.7-1.25)/1.25, P=.04), illicit drug-use days (34% reduction, (2.14-3.22)/3.22, P=.01), quality of life, human immunodeficiency virus screening rates, and number of hospitalizations. Through Seva, patients also provided peer support to one another in ways that are novel in primary care settings. Adoption-Patients sustained high levels of Seva use-between 53% and 60% of the patients at the 3 sites accessed Seva during the last week of the 12-month implementation period. Among clinicians, use of the technology was less robust than use by patients, with only a handful of clinicians using Seva in each clinic and behavioral health providers making most referrals to Seva in 2 of the 3 clinics. Implementation-At 2 sites, implementation plans were realized successfully; they were delayed in the third. Maintenance-Use of Seva dropped when grant funding stopped paying for the mobile phones and data plans. Two of the 3 clinics wanted to maintain the use of Seva, but they struggled to find funding to support this. CONCLUSIONS:Implementing an mHealth system can improve care among primary care patients with SUDs, and patients using the system can support one another in their recovery. Among clinicians, however, implementation requires figuring out how information from the mHealth system will be used and making mHealth data available in the electronic health (eHealth) record. In addition, paying for an mHealth system remains a challenge.

Project description: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).

Project description:BackgroundChatbots have the potential to increase people's access to quality health care. However, the implementation of chatbot technology in the health care system is unclear due to the scarce analysis of publications on the adoption of chatbot in health and medical settings.ObjectiveThis paper presents a protocol of a bibliometric analysis aimed at offering the public insights into the current state and emerging trends in research related to the use of chatbot technology for promoting health.MethodsIn this bibliometric analysis, we will select published papers from the databases of CINAHL, IEEE Xplore, PubMed, Scopus, and Web of Science that pertain to chatbot technology and its applications in health care. Our search strategy includes keywords such as "chatbot," "virtual agent," "virtual assistant," "conversational agent," "conversational AI," "interactive agent," "health," and "healthcare." Five researchers who are AI engineers and clinicians will independently review the titles and abstracts of selected papers to determine their eligibility for a full-text review. The corresponding author (ZN) will serve as a mediator to address any discrepancies and disputes among the 5 reviewers. Our analysis will encompass various publication patterns of chatbot research, including the number of annual publications, their geographic or institutional distribution, and the number of annual grants supporting chatbot research, and further summarize the methodologies used in the development of health-related chatbots, along with their features and applications in health care settings. Software tool VOSViewer (version 1.6.19; Leiden University) will be used to construct and visualize bibliometric networks.ResultsThe preparation for the bibliometric analysis began on December 3, 2021, when the research team started the process of familiarizing themselves with the software tools that may be used in this analysis, VOSViewer and CiteSpace, during which they consulted 3 librarians at the Yale University regarding search terms and tentative results. Tentative searches on the aforementioned databases yielded a total of 2340 papers. The official search phase started on July 27, 2023. Our goal is to complete the screening of papers and the analysis by February 15, 2024.ConclusionsArtificial intelligence chatbots, such as ChatGPT (OpenAI Inc), have sparked numerous discussions within the health care industry regarding their impact on human health. Chatbot technology holds substantial promise for advancing health care systems worldwide. However, developing a sophisticated chatbot capable of precise interaction with health care consumers, delivering personalized care, and providing accurate health-related information and knowledge remain considerable challenges. This bibliometric analysis seeks to fill the knowledge gap in the existing literature on health-related chatbots, entailing their applications, the software used in their development, and their preferred functionalities among users.International registered report identifier (irrid)PRR1-10.2196/54349.

Project description:BackgroundRandomized trials of complex interventions are increasingly including qualitative components to further understand factors that contribute to their success. In this paper, we explore the experiences of health care practitioners in a province wide smoking cessation program (the Smoking Treatment for Ontario Patients program) who participated in the COMBAT trial. This trial examined if the addition of an electronic prompt embedded in a Clinical Decision Support System (CDSS)-designed to prompt practitioners to Screen, provide a Brief intervention and Referral to Treatment (SBIRT) to patients who drank alcohol above the amounts recommended by the Canadian Cancer Society guidelines-influenced the proportion of practitioners delivering a brief intervention to their eligible patients. We wanted to understand the factors influencing implementation and acceptability of delivering a brief alcohol intervention for treatment-seeking smokers for health care providers who had access to the CDSS (intervention arm) and those who did not (control arm).MethodsTwenty-three health care practitioners were selected for a qualitative interview using stratified purposeful sampling (12 from the control arm and 11 from the intervention arm). Interviews were 45 to 90 min in length and conducted by phone using an interview guide that was informed by the National Implementation Research Network's Hexagon tool. Interview recordings were transcribed and coded iteratively between three researchers to achieve consensus on emerging themes. The preliminary coding structure was developed using the National Implementation Research Network's Hexagon Tool framework and data was analyzed using the framework analysis approach.ResultsSeventy eight percent (18/23) of the health care practitioners interviewed recognized the need to simultaneously address alcohol and tobacco use. Seventy four percent (17/23), were knowledgeable about the evidence of health risks associated with dual alcohol and tobacco use but 57% (13/23) expressed concerns with using the Canadian Cancer Society guidelines to screen for alcohol use. Practitioners acknowledged the value of adding a validated screening tool to the STOP program's baseline questionnaire (19/23); however, following through with a brief intervention and referral to treatment proved challenging due to lack of training, limited time, and fear of stigmatizing patients. Practitioners in the intervention arm (5/11; 45%) might not follow the recommendations from CDSS if these recommendations are not perceived as beneficial to the patients.ConclusionsThe results of the study show that practitioners' beliefs were reflective of the current social norms around alcohol use and this influenced their decision to offer a brief alcohol intervention. Future interventions need to emphasize both organizational and sociocultural factors as part of the design. The results of this study point to the need to change social norms regarding alcohol in order to effectively implement interventions that target both alcohol and tobacco use in primary care clinics. Trial registration ClinicalTrials.gov NCT03108144. Retrospectively registered 11 April 2017, https://www.clinicaltrials.gov/ct2/show/NCT03108144.

Project description:BackgroundIn 2019, the Philippines enacted a universal health coverage law that aimed to establish an integrated health system centered around robust primary care as a core strategy of its health system reform agenda. Although the COVID-19 pandemic disrupted initial progress in the reform process, it also presented an opportunity to pilot interventions to demonstrate integration in various ways.MethodsWe conducted a participatory implementation research study to integrate selected public health interventions into the implementation of the primary care benefit package funded by public health insurance. The study was conducted from October 2022 to April 2023 in the Province of Iloilo, Philippines. Entry points within the primary care service delivery process were identified, and interventions related to COVID-19 vaccination and family planning were implemented and monitored. We used the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework to organize the results and present the analysis.ResultsThe intervention showed substantial improvement across the 5 dimensions of the RE-AIM framework, including enhanced access to health care services, as indicated by improvements in primary care patient registration, family planning services, and COVID-19 vaccination processes, as well as an increase in registrations and first patient encounters. These improvements corresponded to sustained primary care facility participation throughout the study period. Additionally, emergent factors that either impeded or facilitated the integration process were identified, providing insights for effectively integrating COVID-19 vaccination within the primary care health system.ConclusionOur study provides evidence of the feasibility of integrating public health interventions into primary care settings. It highlights the potential of using existing primary care service delivery and financing mechanisms as entry points for integration. However, further iteration of the model is required to identify specific conditions for success that can be applied in other contexts and settings.

Project description:BACKGROUND:Clustering of multiple lifestyle risk behaviors has been associated with a greater risk of noncommunicable diseases and mortality than one lifestyle risk behavior or no lifestyle risk behaviors. The National Board of Health and Welfare in Sweden reported in 2018 that it is important to provide additional support to individuals with multiple lifestyle risk behaviors, as risks from these behaviors are multiplicative rather than additive. However, the same report emphasized that there is a lack of knowledge regarding interventions that support changes to unhealthy lifestyle behaviors. OBJECTIVE:The MoBILE (Mobile health Multiple lifestyle Behavior Interventions across the LifEspan) research program has brought together two Swedish research groups supported by international collaborators. Through this collaboration, we aim to design and evaluate a number of novel and tailored mobile health (mHealth) multiple lifestyle behavior interventions across the life span of different health care populations. In addition, the MoBILE research program will extend ongoing research to include mHealth interventions for migrant pregnant women and children. METHODS:Each project within the MoBILE program will focus on a specific group: pregnant women, preschool children, high school and university students, and adults in primary and clinical care. All the projects will follow the same 4 phases: requirements, development, evaluation, and implementation. During the requirements phase, implementers and end users will aid the design of content and functionality of the interventions. In the development phase, findings from the first phase will be synthesized with expert domain knowledge and theoretical constructs to create interventions tailored to the target groups. The third phase, evaluation, will comprise randomized controlled trials conducted to estimate the effects of the interventions on multiple lifestyle risk behaviors (eg, alcohol, nutrition, physical activity, and smoking). The final phase will investigate how the interventions, if found effective, can be disseminated into different health care contexts. RESULTS:The research program commenced in 2019, and the first results will be available in 2020. Projects involving pregnant women, preschool children, and high school and university students will be completed in the first 3 years, with the remaining projects being planned for the program's final 3 years. CONCLUSIONS:The development of evidence-based digital tools is complex, as they should be guided by theoretical frameworks, and requires large interdisciplinary teams with competence in technology, behavioral science, and lifestyle-specific areas. Individual researchers or smaller research groups developing their own tools is not the way forward, as it means reinventing the wheel over and over again. The MoBILE research program therefore aims to join forces and learn from the past 10 years of mHealth research to maximize scientific outcomes, as well as the use of financial resources to expand the growing body of evidence for mHealth lifestyle behavior interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):PRR1-10.2196/14894.

Project description:IntroductionAmid the rising number of people with non-communicable diseases (NCDs), Kenya has invested in strengthening primary care and in efforts to expand existing service delivery platforms to integrate NCD care. One such approach is the AMPATH (Academic Model Providing Access to Healthcare) model in western Kenya, which provides the platform for the Primary Health Integrated Care Project for Chronic Conditions (PIC4C), launched in 2018 to further strengthen primary care services for the prevention and control of hypertension, diabetes, breast and cervical cancer. This study seeks to understand how well PIC4C delivers on its intended aims and to inform and support scale up of the PIC4C model for integrated care for people with NCDs in Kenya.Methods and analysisThe study is guided by a conceptual framework on implementing, sustaining and spreading innovation in health service delivery. We use a multimethod design combining qualitative and quantitative approaches, involving: (1) in-depth interviews with health workers and decision-makers to explore experiences of delivering PIC4C; (2) a cross-sectional survey of patients with diabetes or hypertension and in-depth interviews to understand how well PIC4C meets patients' needs; (3) a cohort study with an interrupted time series analysis to evaluate the degree to which PIC4C leads to health benefits such as improved management of hypertension or diabetes; and (4) a cohort study of households to examine the extent to which the national hospital insurance chronic care package provides financial risk protection to people with hypertension or diabetes within PIC4C.Ethics and disseminationThe study has received approvals from Moi University Institutional Research and Ethics Committee (FAN:0003586) and the London School of Hygiene & Tropical Medicine (17940). Workshops with key stakeholders at local, county, national and international levels will ensure early and wide dissemination of our findings to inform scale up of this model of care. We will also publish findings in peer-reviewed journals.

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Integrating addiction treatment into primary care using mobile health technology: protocol for an implementation research study.

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Integrating addiction treatment into primary care using mobile health technology: protocol for an implementation research study.

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