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Evaluating the Implementation and Feasibility of a Web-Based Tool to Support Timely Identification and Care for the Frail Population in Primary Healthcare Settings.

ABSTRACT: Understanding and addressing the needs of frail persons is an emerging health priority for Nova Scotia and internationally. Primary healthcare (PHC) providers regularly encounter frail persons in their daily clinical work. However, routine identification and measurement of frailty is not standard practice and, in general, there is a lack of awareness about how to identify and respond to frailty. A web-based tool called the Frailty Portal was developed to aid in identifying, screening, and providing care for frail patients in PHC settings. In this study, we will assess the implementation feasibility and impact of the Frailty Portal to: (1) support increased awareness of frailty among providers and patients, (2) identify the degree of frailty within individual patients, and (3) develop and deliver actions to respond to frailtyl in community PHC practice.This study will be approached using a convergent mixed method design where quantitative and qualitative data are collected concurrently, in this case, over a 9-month period, analyzed separately, and then merged to summarize, interpret and produce a more comprehensive understanding of the initiative's feasibility and scalability. Methods will be informed by the 'Implementing the Frailty Portal in Community Primary Care Practice' logic model and questions will be guided by domains and constructs from an implementation science framework, the Consolidated Framework for Implementation Research (CFIR).The 'Frailty Portal' aims to improve access to, and coordination of, primary care services for persons experiencing frailty. It also aims to increase primary care providers' ability to care for patients in the context of their frailty. Our goal is to help optimize care in the community by helping community providers gain the knowledge they may lack about frailty both in general and in their practice, support improved identification of frailty with the use of screening tools, offer evidence based severity-specific care goals and connect providers with local available community supports.

SUBMITTER: Lawson B

PROVIDER: S-EPMC5505107 | biostudies-other | 2017 Mar

REPOSITORIES: biostudies-other

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Project description:BackgroundRehabilitation potential involves predicting who will benefit from rehabilitation. Decisions about rehabilitation potential must take into account personal, clinical and contextual factors, a process which is complicated in the presence of acute ill-health and frailty. This study aimed to evaluate the feasibility and acceptability of the Rehabilitation Potential Assessment Tool (RePAT) - a 15 item holistic, person-centred assessment tool and training package - in the acute hospital setting.MethodsA non-randomised feasibility study with nested semi-structured interviews explored whether RePAT was feasible and acceptable. Feasibility was tested by recruiting physiotherapy and occupational therapy participants delivering the RePAT intervention to patients alongside usual clinical care. Acceptability was tested by conducting semi-structured interviews with staff, patient and carer participants. Staff and patient characteristics were analysed using descriptive statistics. Interview data were analysed thematically. Fidelity of completed RePAT items was assessed against a priori criteria on how closely they matched tool guidance by two researchers. Mean values of the two scores were calculated. RePAT content was analysed and supported with verbatim quotes.ResultsSix staff participants were recruited and trained. They assessed 26 patient participants using RePAT. Mean (SD) patient age was 86.16 (±6.39) years. 32% were vulnerable or mildly frail, 42% moderately frail and 26% severely or very severely frail using the Clinical Frailty Scale. Mean (SD) time to complete RePAT was 32.7 (±9.6) minutes. RePAT fidelity was good where 13 out of 15 items achieved a priori fidelity. RePAT was acceptable and tolerated by staff and patients. Staff participants reported RePAT enabled them to consider rehabilitation decisions in a more structured and consistent way. Patients and carer participants, although unable to comment directly on RePAT, reported being satisfied with their rehabilitation assessments which were found to embrace a person-centred approach.ConclusionsRePAT was found to be acceptable and feasible by staff, carers and patients. It allowed clinicians to make explicit their reasoning behind rehabilitation assessments and encouraged them to become more cognisant of factors which affected their clinical decision-making.Trial registrationID ISRCTN31938453 . Registered 05/10/2021.

Project description:Population datasets and the Internet are playing an ever-growing role in the way cancer information is made available to providers, patients, and their caregivers. The Surveillance, Epidemiology, and End Results Cancer Survival Calculator (SEER*CSC) is a Web-based cancer prognostic tool that uses SEER data, a large population dataset, to provide physicians with highly valid, evidence-based prognostic estimates for increasing shared decision-making and improving patient-provider communication of complex health information.The aim of this study was to develop, test, and implement SEER*CSC.An iterative approach was used to develop the SEER*CSC. Based on input from cancer patient advocacy groups and physicians, an initial version of the tool was developed. Next, providers from 4 health care delivery systems were recruited to do formal usability testing of SEER*CSC. A revised version of SEER*CSC was then implemented in two health care delivery sites using a real-world clinical implementation approach, and usage data were collected. Post-implementation follow-up interviews were conducted with site champions. Finally, patients from two cancer advocacy groups participated in usability testing.Overall feedback of SEER*CSC from both providers and patients was positive, with providers noting that the tool was professional and reliable, and patients finding it to be informational and helpful to use when discussing their diagnosis with their provider. However, use during the small-scale implementation was low. Reasons for low usage included time to enter data, not having treatment options in the tool, and the tool not being incorporated into the electronic health record (EHR). Patients found the language in its current version to be too complex.The implementation and usability results showed that participants were enthusiastic about the use and features of SEER*CSC, but sustained implementation in a real-world clinical setting faced significant challenges. As a result of these findings, SEER*CSC is being redesigned with more accessible language for a public facing release. Meta-tools, which put different tools in context of each other, are needed to assist in understanding the strengths and limitations of various tools and their place in the clinical decision-making pathway. The continued development and eventual release of prognostic tools should include feedback from multidisciplinary health care teams, various stakeholder groups, patients, and caregivers.

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:BackgroundApproximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence-powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows.ObjectiveThis study aims to examine the feasibility of an artificial intelligence-powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network-based individualized treatment remission prediction.MethodsOwing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews.ResultsData were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F2,24=0.805; mean squared error 58.08; P=.46). In total, 92% (12/13) of patients and 71% (5/7) of physicians felt that the tool was easy to use; 62% (8/13) of patients and 71% (5/7) of physicians rated that they trusted the CDSS. Of the 13 patients, 6 (46%) felt that the patient-clinician relationship significantly or somewhat improved, whereas 7 (54%) felt that it did not change.ConclusionsOur findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies.Trial registrationClinicalTrials.gov NCT04061642; http://clinicaltrials.gov/ct2/show/NCT04061642.

Project description:BackgroundFor many reasons, the emergency department (ED) is a critical venue to initiate OUD interventions. The prevailing culture of the ED has been that substance use disorders are non-emergent conditions better addressed outside the ED where resources are less constrained. This study, its rapid funding mechanism, and accelerated timeline originated out of the urgent need to learn whether ED-initiated buprenorphine (BUP) with referral for treatment of OUD is generalizable, as well as to develop strategies to facilitate its adoption across a variety of ED settings and under real-world conditions. It both complements and uses methods adapted from Project ED Health (CTN-0069), a Hybrid Type 3 implementation-effectiveness study of using Implementation Facilitation (IF) to integrate ED-initiated BUP and referral programs.MethodsED-CONNECT (CTN 0079) was a three-site implementation study exploring the feasibility, acceptability, and impact of introducing ED-initiated BUP in rural and urban settings with high-need, limited resources, and different staffing structures. We used a multi-faceted approach to develop, introduce and iteratively refine site-specific ED clinical protocols and implementation plans for opioid use disorder (OUD) screening, ED-initiated BUP, and referral for treatment. We employed a participatory action research approach and use mixed methods incorporating data derived from abstraction of medical records and administrative data, assessments of recruited ED patient-participants, and both qualitative and quantitative inquiry involving staff from the ED and community, patients, and other stakeholders.DiscussionThis study was designed to provide the necessary, time-sensitive understanding of how to identify OUD and initiate treatment with BUP in the EDs previously not providing ED-initiated BUP, in communities in which this intervention is most needed: high need, low resource settings.Trial registrationThe study was prospectively registered on ClinicalTrials.gov (NCT03544112) on June 01, 2018: https://clinicaltrials.gov/ct2/show/NCT03544112 .

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:ObjectiveTo evaluate the effectiveness of feedback reports and feedback reports + external facilitation on completion of life-sustaining treatment (LST) note the template and durable medical orders. This quality improvement program supported the national roll-out of the Veterans Health Administration (VA) LST Decisions Initiative (LSTDI), which aims to ensure that seriously-ill veterans have care goals and LST decisions elicited and documented.Data sourcesPrimary data from national databases for VA nursing homes (called Community Living Centers [CLCs]) from 2018 to 2020.Study designIn one project, we distributed monthly feedback reports summarizing LST template completion rates to 12 sites as the sole implementation strategy. In the second involving five sites, we distributed similar feedback reports and provided robust external facilitation, which included coaching, education, and learning collaboratives. For each project, principal component analyses matched intervention to comparison sites, and interrupted time series/segmented regression analyses evaluated the differences in LSTDI template completion rates between intervention and comparison sites.Data collection methodsData were extracted from national databases in addition to interviews and surveys in a mixed-methods process evaluation.Principal findingsLSTDI template completion rose from 0% to about 80% throughout the study period in both projects' intervention and comparison CLCs. There were small but statistically significant differences for feedback reports alone (comparison sites performed better, coefficient estimate 3.48, standard error 0.99 for the difference between groups in change in trend) and feedback reports + external facilitation (intervention sites performed better, coefficient estimate -2.38, standard error 0.72).ConclusionsFeedback reports + external facilitation was associated with a small but statistically significant improvement in outcomes compared with comparison sites. The large increases in completion rates are likely due to the well-planned national roll-out of the LSTDI. This finding suggests that when dissemination and support for widespread implementation are present and system-mandated, significant enhancements in the adoption of evidence-based practices may require more intensive support.

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Evaluating the Implementation and Feasibility of a Web-Based Tool to Support Timely Identification and Care for the Frail Population in Primary Healthcare Settings.

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