BMC Medical Informatics and Decision Making最新文献

Background: Interest in mental health smartphone applications has grown in recent years. Despite their effectiveness and advantages, special attention needs to be paid to two aspects to ensure app engagement: to include patients and professionals in their design and to guarantee their usability. The aim of this study was to analyse the perceived usability and quality of the preliminary version of RegulEm, an app based in the Unified Protocol, as part of the second stage of the app development.

Methods: A parallel mixed methods study was used with 7 professionals and 4 users who were previously involved in the first stage of the development of the app. MARS, uMARS and SUS scales were used, and two focus groups were conducted. Descriptive statistical analysis and a thematic content analysis were performed in order to gather as much information as possible on RegulEm's usability and quality as well as suggestions for improvement.

Results: RegulEm's usability was perceived through the SUS scale scores as good by users (75 points) and excellent by professionals (84.64 points), while its quality was perceived through the uMARS and MARS scales as good by both groups, with 4 and 4.14 points out of 5. Different areas regarding RegulEm's usability and suggestions for improvement were identified in both focus groups and 20% of the suggestions proposed were implemented in the refined version of RegulEm.

Conclusion: RegulEm's usability and quality were perceived as good by users and professionals and different identified areas have contributed to its refinement. This study provides a more complete picture of RegulEm's usability and quality prior analysing its effectiveness, implementation and cost-effectiveness in Spanish public mental health units.

Background: Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer. Its epidemiological features, treatment principles, and prognostic factors remain controversial.

Objective: This study aimed to develop an improved machine learning-based model to predict the prognosis of patients with invasive micropapillary carcinoma.

Methods: A total of 1123 patients diagnosed with IMPC after surgery between 1998 and 2019 were identified from the Surveillance, Epidemiology, and End Results (SEER) database for survival analysis. Univariate and multivariate analyses were performed to explore independent prognostic factors for the overall and disease-specific survival of patients with IMPC. Five machine learning algorithms were developed to predict the 5-year survival of these patients.

Results: Cox regression analysis indicated that patients aged > 65 years had a significantly worse prognosis than those younger in age, while unmarried patients had a better prognosis than married patients. Patients diagnosed between 2001 and 2005 had a significant risk reduction of mortality compared with other periods. The XGBoost model outperformed the other models with a precision of 0.818 and an area under the curve of 0.863.

Conclusions: A machine learning model for IMPC in patients with breast cancer was developed to estimate the 5-year OS. The XGBoost model had a promising performance and can help clinicians determine the early prognosis of patients with IMPC; therefore, the model can improve clinical outcomes by influencing management strategies and patient health care decisions.

Background: Women with high-risk breast lesions, such as atypical hyperplasia (AH) or lobular carcinoma in situ (LCIS), have a 4- to tenfold increased risk of breast cancer compared to women with non-proliferative breast disease. Despite high-quality data supporting chemoprevention, uptake remains low. Interventions are needed to break down barriers.

Methods: The parent trial, MiCHOICE, is a cluster randomized controlled trial evaluating the effectiveness and implementation of patient and provider decision support tools to improve informed choice about chemoprevention among women with AH or LCIS. For this pre-implementation analysis, 25 providers participated in semi-structured interviews prior to accessing decision support tools. Interviews sought to understand attitudes/beliefs and barriers/facilitators to chemoprevention.

Results: Interviews with 25 providers (18 physicians and 7 advanced practice providers) were included. Providers were predominantly female (84%), white (72%), and non-Hispanic (88%). Nearly all providers (96%) had prescribed chemoprevention for eligible patients. Three themes emerged in qualitative analysis. The first theme describes providers' confidence in chemoprevention and the utility of decision support tools. The second theme elucidates barriers to chemoprevention, including time constraints, risk communication and perceptions of patients' fear of side effects and anxiety. The third theme is the need for early implementation of decision support tools.

Conclusions: This qualitative study suggests that providers were interested in the early inclusion of decision aids (DA) in their chemoprevention discussion workflow. The DAs may help overcome certain barriers which were elucidated in these interviews, including patient level concerns about side effects, clinic time constraints and difficulty communicating risk. A multi-faceted intervention with a DA as one active component may be needed.

Trial registration: This trial was registered with the NIH clinical trial registry, clinicaltrials.gov, NCT04496739.

Background: Recognizing the limitations of pre-market clinical data, regulatory authorities have embraced total product lifecycle management with post-market surveillance (PMS) data to assess medical device safety and performance. One method of proactive PMS involves the analysis of real-world data (RWD) through retrospective review of electronic health records (EHR). Because EHRs are patient-centered and focused on providing tools that clinicians use to determine care rather than collecting information on individual medical products, the process of transforming RWD into real-world evidence (RWE) can be laborious, particularly for medical devices with broad clinical use and extended clinical follow-up. This study describes a method to extract RWD from EHR to generate RWE on the safety and performance of embolization coils.

Methods: Through a partnership between a non-profit data institute and a medical device manufacturer, information on implantable embolization coils' use was extracted, linked, and analyzed from clinical data housed in an electronic data warehouse from the state of Indiana's largest health system. To evaluate the performance and safety of the embolization coils, technical success and safety were defined as per the Society of Interventional Radiology guidelines. A multi-prong strategy including electronic and manual review of unstructured (clinical chart notes) and structured data (International Classification of Disease codes), was developed to identify patients with relevant devices and extract data related to the endpoints.

Results: A total of 323 patients were identified as treated using Cook Medical Tornado, Nester, or MReye embolization coils between 1 January 2014 and 31 December 2018. Available clinical follow-up for these patients was 1127 ± 719 days. Indications for use, adverse events, and procedural success rates were identified via automated extraction of structured data along with review of available unstructured data. The overall technical success rate was 96.7%, and the safety events rate was 5.3% with 18 major adverse events in 17 patients. The calculated technical success and safety rates met pre-established performance goals (≥ 85% for technical success and ≤ 12% for safety), highlighting the relevance of this surveillance method.

Conclusions: Generating RWE from RWD requires careful planning and execution. The process described herein provided valuable longitudinal data for PMS of real-world device safety and performance. This cost-effective approach can be translated to other medical devices and similar RWD database systems.