Dataset Information

A Web-Based Decision Tool to Improve Contraceptive Counseling for Women With Chronic Medical Conditions: Protocol For a Mixed Methods Implementation Study.

ABSTRACT: BACKGROUND:Women with chronic medical conditions, such as diabetes and hypertension, have a higher risk of pregnancy-related complications compared with women without medical conditions and should be offered contraception if desired. Although evidence based guidelines for contraceptive selection in the presence of medical conditions are available via the United States Medical Eligibility Criteria (US MEC), these guidelines are underutilized. Research also supports the use of decision tools to promote shared decision making between patients and providers during contraceptive counseling. OBJECTIVE:The overall goal of the MiHealth, MiChoice project is to design and implement a theory-driven, Web-based tool that incorporates the US MEC (provider-level intervention) within the vehicle of a contraceptive decision tool for women with chronic medical conditions (patient-level intervention) in community-based primary care settings (practice-level intervention). This will be a 3-phase study that includes a predesign phase, a design phase, and a testing phase in a randomized controlled trial. This study protocol describes phase 1 and aim 1, which is to determine patient-, provider-, and practice-level factors that are relevant to the design and implementation of the contraceptive decision tool. METHODS:This is a mixed methods implementation study. To customize the delivery of the US MEC in the decision tool, we selected high-priority constructs from the Consolidated Framework for Implementation Research and the Theoretical Domains Framework to drive data collection and analysis at the practice and provider level, respectively. A conceptual model that incorporates constructs from the transtheoretical model and the health beliefs model undergirds patient-level data collection and analysis and will inform customization of the decision tool for this population. We will recruit 6 community-based primary care practices and conduct quantitative surveys and semistructured qualitative interviews with women who have chronic medical conditions, their primary care providers (PCPs), and clinic staff, as well as field observations of practice activities. Quantitative survey data will be summarized with simple descriptive statistics and relationships between participant characteristics and contraceptive recommendations (for PCPs), and current contraceptive use (for patients) will be examined using Fisher exact test. We will conduct thematic analysis of qualitative data from interviews and field observations. The integration of data will occur by comparing, contrasting, and synthesizing qualitative and quantitative findings to inform the future development and implementation of the intervention. RESULTS:We are currently enrolling practices and anticipate study completion in 15 months. CONCLUSIONS:This protocol describes the first phase of a multiphase mixed methods study to develop and implement a Web-based decision tool that is customized to meet the needs of women with chronic medical conditions in primary care settings. Study findings will promote contraceptive counseling via shared decision making and reflect evidence-based guidelines for contraceptive selection. TRIAL REGISTRATION:ClinicalTrials.gov NCT03153644; https://clinicaltrials.gov/ct2/show/NCT03153644 (Archived by WebCite at http://www.webcitation.org/6yUkA5lK8).

SUBMITTER: Wu JP

PROVIDER: S-EPMC5932336 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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A Web-Based Decision Tool to Improve Contraceptive Counseling for Women With Chronic Medical Conditions: Protocol For a Mixed Methods Implementation Study.

Wu Justine P JP Damschroder Laura J LJ Fetters Michael D MD Zikmund-Fisher Brian J BJ Crabtree Benjamin F BF Hudson Shawna V SV Ruffin Mack T MT Fucinari Juliana J Kang Minji M Taichman L Susan LS Creswell John W JW

JMIR research protocols 20180418 4

<h4>Background</h4>Women with chronic medical conditions, such as diabetes and hypertension, have a higher risk of pregnancy-related complications compared with women without medical conditions and should be offered contraception if desired. Although evidence based guidelines for contraceptive selection in the presence of medical conditions are available via the United States Medical Eligibility Criteria (US MEC), these guidelines are underutilized. Research also supports the use of decision too ...[more]

PMID: 29669707

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Project description:BackgroundTo achieve informed choice within the framework of reproductive autonomy, family planning programs have begun to adopt user-centered approaches to service provision, which highlight the individual client as the focal point of interaction and key decision maker. However, little is known about how user-centered approaches to family planning, particularly family planning counseling, shape contraceptive preferences and choices.ObjectiveWe conducted a multiarmed randomized controlled trial to identify the causal impact of user-centered approaches to family planning counseling on women's contraceptive decision making in urban Malawi. This study aims to determine how a tailored, preference-driven approach to family planning counseling and the involvement of male partners during the counseling process may contribute to shaping women's contraceptive preferences and choices.MethodsMarried women aged 18-35 years were recruited and randomly assigned to 1 of the 3 intervention arms or a control arm characterized by the following two interventions: an intervention arm in which women were encouraged to invite their husbands to family planning counseling (husband invitation arm) and an intervention arm in which women received targeted, tailored counseling on up to five contraceptive methods (as opposed to up to 13 contraceptive methods) that reflected women's stated preferences for contraceptive methods. Women were randomized into a control arm, T0 (no husband invitation, standard counseling); T1 (husband invitation, standard counseling); T2 (no husband invitation, targeted counseling); and T3 (husband invitation, targeted counseling). Following counseling, all women received a package of family planning services, which included free transportation to a local family planning clinic and financial reimbursement for family planning services. Follow-up surveys were conducted with women 1 month after counseling.ResultsA total of 785 women completed the baseline survey, and 782 eligible respondents were randomized to 1 of the 3 intervention groups or the control group (T1, n=223; T2, n=225; T3, n=228; T0, n=108). Furthermore, 98.1% (767/782) of women were contacted for follow-up. Among the 767 women who were contacted, 95.3% (731/767) completed the follow-up survey. The analysis of the primary outcomes is ongoing and is expected to be completed by the end of 2021.ConclusionsThe results from this trial will fill knowledge gaps on the effectiveness of tailored family planning counseling and male involvement in family planning on women's stated and realized contraceptive preferences. More generally, the study will provide evidence on how user-centered counseling may affect women's willingness to use and continue contraception to realize their contraceptive preferences.Trial registrationAmerican Economics Association's Registry for Randomized Controlled Trials AEARCTR-0004194; https://www.socialscienceregistry.org/trials/4194/history/46808. Registry for International Development Impact Evaluations RIDIE-STUDY-ID-5ce4f42bbc2bf; https://ridie.3ieimpact.org/index.php?r=search/detailView&id=823.International registered report identifier (irrid)DERR1-10.2196/24884.

Project description:BackgroundApproximately 30% of intubated preterm infants with respiratory distress syndrome (RDS) will fail attempted extubation, requiring reintubation and mechanical ventilation. Although ventilator technology and monitoring of premature infants have improved over time, optimal extubation remains challenging. Furthermore, extubation decisions for premature infants require complex informational processing, techniques implicitly learned through clinical practice. Computer-aided decision-support tools would benefit inexperienced clinicians, especially during peak neonatal intensive care unit (NICU) census.MethodsA five-step procedure was developed to identify predictive variables. Clinical expert (CE) thought processes comprised one model. Variables from that model were used to develop two mathematical models for the decision-support tool: an artificial neural network (ANN) and a multivariate logistic regression model (MLR). The ranking of the variables in the three models was compared using the Wilcoxon Signed Rank Test. The best performing model was used in a web-based decision-support tool with a user interface implemented in Hypertext Markup Language (HTML) and the mathematical model employing the ANN.ResultsCEs identified 51 potentially predictive variables for extubation decisions for an infant on mechanical ventilation. Comparisons of the three models showed a significant difference between the ANN and the CE (p = 0.0006). Of the original 51 potentially predictive variables, the 13 most predictive variables were used to develop an ANN as a web-based decision-tool. The ANN processes user-provided data and returns the prediction 0-1 score and a novelty index. The user then selects the most appropriate threshold for categorizing the prediction as a success or failure. Furthermore, the novelty index, indicating the similarity of the test case to the training case, allows the user to assess the confidence level of the prediction with regard to how much the new data differ from the data originally used for the development of the prediction tool.ConclusionState-of-the-art, machine-learning methods can be employed for the development of sophisticated tools to aid clinicians' decisions. We identified numerous variables considered relevant for extubation decisions for mechanically ventilated premature infants with RDS. We then developed a web-based decision-support tool for clinicians which can be made widely available and potentially improve patient care world wide.

Project description:BackgroundUp to one-third of young people live with chronic physical conditions (eg, diabetes, asthma, and autoimmune disease) that frequently involve recurrent pain, fatigue, activity limitations, stigma, and isolation. These issues may be exacerbated as young people transition through adolescence. Accordingly, young people with chronic illness are at a high risk of psychological distress. Accessible, evidence-based interventions for young people with chronic illnesses are urgently needed to improve well-being, support adaptation, and enhance daily functioning. Self-compassion, which is an adaptive means of relating to oneself during times of difficulty, is a promising intervention target for this population.ObjectiveThis study aims to test the efficacy of a 4-week, self-guided, web-based self-compassion training program for improving well-being among young Australians (aged 16-25 years) living with a chronic medical condition. The primary outcomes were self-compassion, emotion regulation difficulties, and coping; the secondary outcomes were well-being, distress, and quality of life. We also sought to test whether changes in primary outcomes mediated changes in secondary outcomes and gather feedback about the strengths and limitations of the program.MethodsWe conducted a single-blind, parallel-group, randomized controlled trial comparing a 4-week, fully automated, web-based self-compassion training program with a waitlist control. Participants were recruited via the internet, and outcomes were self-assessed at 4 (T1) and 12 weeks (T2) after the baseline time point via a web-based survey. A mixed methods approach was used to evaluate the program feedback.ResultsOverall, 151 patients (age: mean 21.15, SD 2.77 years; female patients: n=132, 87.4%) were randomized to the intervention (n=76, 50.3%) and control (n=75, 49.7%) groups. The loss-to-follow-up rate was 47.4%, and program use statistics indicated that only 29% (22/76) of young people in the experimental group completed 100% of the program. The main reported barrier to completion was a lack of time. As anticipated, treatment effects were observed for self-compassion (P=.01; partial η2=0.05; small effect); well-being (P≤.001; partial η2=0.07; medium effect); and distress (P=.003; partial η2=0.054; small-medium effect) at the posttest time point and maintained at follow-up. Contrary to our hypotheses, no intervention effects were observed for emotion regulation difficulties or maladaptive coping strategies. Improvements in adaptive coping were observed at the posttest time point but were not maintained at follow-up. Self-compassion, but not emotion regulation difficulties or coping, mediated the improvements in well-being.ConclusionsMinimal-contact, web-based self-compassion training can confer mental health benefits on young people with chronic conditions. This group experiences substantial challenges to participation in mental health supports, and program engagement and retention in this trial were suboptimal. Future work should focus on refining the program content, engagement, and delivery to optimize engagement and treatment outcomes for the target group.Trial registrationAustralian New Zealand Clinical Trials Registry 12619000572167; https://tinyurl.com/5n6hevt.International registered report identifier (irrid)RR2-10.1186/s12889-020-8226-7.

Project description:ObjectiveTo implement artificial neural network (ANN) algorithms for noninvasive lymph node staging (NILS) to a decision support tool and facilitate the option to omit surgical axillary staging in breast cancer patients with low-risk of nodal metastasis.MethodsThe NILS tool is a further development of an ANN prototype for the prediction of nodal status. Training and internal validation of the original algorithm included 15 clinical and tumor-related variables from a consecutive cohort of 800 breast cancer cases. The updated NILS tool included 10 top-ranked input variables from the original prototype. A workflow with four ANN pathways was additionally developed to allow different combinations of missing preoperative input values. Predictive performances were assessed by area under the receiver operating characteristics curves (AUC) and sensitivity/specificity values at defined cut-points. Clinical utility was presented by estimating possible sentinel lymph node biopsy (SLNB) reduction rates. The principles of user-centered design were applied to develop an interactive web-interface to predict the patient's probability of healthy lymph nodes. A technical validation of the interface was performed using data from 100 test patients selected to cover all combinations of missing histopathological input values.ResultsANN algorithms for the prediction of nodal status have been implemented into the web-based NILS tool for personalized, noninvasive nodal staging in breast cancer. The estimated probability of healthy lymph nodes using the interface showed a complete concordance with estimations from the reference algorithm except in two cases that had been wrongly included (ineligible for the technical validation). NILS predictive performance to distinguish node-negative from node-positive disease, also with missing values, displayed AUC ranged from 0.718 (95% CI, 0.687-0.748) to 0.735 (95% CI, 0.704-0.764), with good calibration. Sensitivity 90% and specificity 34% were demonstrated. The potential to abstain from axillary surgery was observed in 26% of patients using the NILS tool, acknowledging a false negative rate of 10%, which is clinically accepted for the standard SLNB technique.ConclusionsThe implementation of NILS into a web-interface are expected to provide the health care with decision support and facilitate preoperative identification of patients who could be good candidates to avoid unnecessary surgical axillary staging.

Project description:BackgroundHomecare settings across the United States provide care to more than 5 million patients every year. About one in five homecare patients are rehospitalized during the homecare episode, with up to two-thirds of these rehospitalizations occurring within the first 2 weeks of services. Timely allocation of homecare services might prevent a significant portion of these rehospitalizations. The first homecare nursing visit is one of the most critical steps of the homecare episode. This visit includes an assessment of the patient's capacity for self-care, medication reconciliation, an examination of the home environment, and a discussion regarding whether a caregiver is present. Hence, appropriate timing of the first visit is crucial, especially for patients with urgent health care needs. However, nurses often have limited and inaccurate information about incoming patients, and patient priority decisions vary significantly between nurses. We developed an innovative decision support tool called Priority for the First Nursing Visit Tool (PREVENT) to assist nurses in prioritizing patients in need of immediate first homecare nursing visits.ObjectiveThis study aims to evaluate the effectiveness of the PREVENT tool on process and patient outcomes and to examine the reach, adoption, and implementation of PREVENT.MethodsEmploying a pre-post design, survival analysis, and logistic regression with propensity score matching analysis, we will test the following hypotheses: compared with not using the tool in the preintervention phase, when homecare clinicians use the PREVENT tool, high-risk patients in the intervention phase will (1) receive more timely first homecare visits and (2) have decreased incidence of rehospitalization and have decreased emergency department use within 60 days. Reach, adoption, and implementation will be assessed using mixed methods including homecare admission staff interviews, think-aloud observations, and analysis of staffing and other relevant data.ResultsThe study research protocol was approved by the institutional review board in October 2019. PREVENT is currently being integrated into the electronic health records at the participating study sites. Data collection is planned to start in early 2021.ConclusionsMixed methods will enable us to gain an in-depth understanding of the complex socio-technological aspects of the hospital to homecare transition. The results have the potential to (1) influence the standardization and individualization of nurse decision making through the use of cutting-edge technology and (2) improve patient outcomes in the understudied homecare setting.Trial registrationClinicalTrials.gov NCT04136951; https://clinicaltrials.gov/ct2/show/NCT04136951.International registered report identifier (irrid)PRR1-10.2196/20184.

Dataset Information

A Web-Based Decision Tool to Improve Contraceptive Counseling for Women With Chronic Medical Conditions: Protocol For a Mixed Methods Implementation Study.

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A Web-Based Decision Tool to Improve Contraceptive Counseling for Women With Chronic Medical Conditions: Protocol For a Mixed Methods Implementation Study.

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