Acceptability, Appropriateness, and Feasibility of Automated Screening Approaches and Family Communication Methods for Identification of Familial Hypercholesterolemia: Stakeholder Engagement Results from the IMPACT-FH Study.
Laney K JonesNicole L WaltersAndrew BranganCatherine D AhmedMichael GatuskyGemme Campbell-SalomeIlene G LaddAmanda SheldonSamuel S GiddingMary P McGowanAlanna K RahmAmy C SturmPublished in: Journal of personalized medicine (2021)
Guided by the Conceptual Model of Implementation Research, we explored the acceptability, appropriateness, and feasibility of: (1) automated screening approaches utilizing existing health data to identify those who require subsequent diagnostic evaluation for familial hypercholesterolemia (FH) and (2) family communication methods including chatbots and direct contact to communicate information about inherited risk for FH. Focus groups were conducted with 22 individuals with FH (2 groups) and 20 clinicians (3 groups). These were recorded, transcribed, and analyzed using deductive (coded to implementation outcomes) and inductive (themes based on focus group discussions) methods. All stakeholders described these initiatives as: (1) acceptable and appropriate to identify individuals with FH and communicate risk with at-risk relatives; and (2) feasible to implement in current practice. Stakeholders cited current initiatives, outside of FH (e.g., pneumonia protocols, colon cancer and breast cancer screenings), that gave them confidence for successful implementation. Stakeholders described perceived obstacles, such as nonfamiliarity with FH, that could hinder implementation and potential solutions to improve systematic uptake of these initiatives. Automated health data screening, chatbots, and direct contact approaches may be useful for patients and clinicians to improve FH diagnosis and cascade screening.
Keyphrases
- quality improvement
- healthcare
- primary care
- mental health
- machine learning
- public health
- high throughput
- deep learning
- health information
- electronic health record
- ejection fraction
- newly diagnosed
- palliative care
- end stage renal disease
- physical activity
- depressive symptoms
- type diabetes
- intensive care unit
- big data
- social media
- prognostic factors
- young adults
- social support
- insulin resistance
- extracorporeal membrane oxygenation
- risk assessment
- mechanical ventilation
- acute respiratory distress syndrome
- bioinformatics analysis