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Incorporating Technology in Community-Based Learning
Unique learning needs exist in all patient populations. However, identifying the need and then designing a technology-based intervention requires a careful analysis of both the population and the planned action. The purpose of this discussion is to describe a community-based population, describe a selected learning need for this population, and explain how I would design a PowerPoint presentation that would address that need.
I work in a Federally Qualified Health Center (FQHC) that provides primary care to patients across the lifespan. FQHCs receive funding from the Health Resources and Services Administration Health Center Program to provide care in medically underserved communities (Health Resources & Services Administration [HRSA], 2018). Over 28 million people receive care at FQHCs in the United States (HRSA, n.d.). FQHCs use a sliding scale fee, ensuring patients have access to affordable medical care (HRSA, 2018). While FQHCs are intended to serve the medically uninsured, over 77% of the patients receiving care at FQHCs have private insurance, Medicare, Medicaid, or another governmentally funded health insurance (HRSA, 2019). Females represent 57.65% of the patient population, with patients between the ages of 25 and 69 years old presenting most frequently (HRSA, 2019). However, the FQHC patient population possesses unique barriers to care. A significant hurdle for FQHCs to overcome is language, as 23.63% speak a language other than English (HRSA, 2019). Additionally, 68.23% of the patient population is at or below the poverty level (HRSA, 2019). Complicating continuity of care with this patient population is the high number of migratory and seasonal workers and homeless individuals receiving care (HRSA, 2019). All combined, these factors create a patient population with sub-par health literacy.
Indigent patient populations are more likely to have decreased health literacy levels (Whitley, Jones, Hansen, & Vora, 2019). Additionally, patients with diminished health literacy are less likely to return for follow-up care as scheduled (Thompson et al., 2015). Patients who are chronically under- or uninsured do not return for follow-up appointments as medical is not always viewed as a necessity. Failure to follow-up for appointments as scheduled has been shown to increase emergency department visits and worsen patient outcomes (Arora et al., 2015). Therefore, a means to address the FQHC patients’ knowledge deficit of the need to return for follow-up appointments should be identified and implemented.
Addressing the Learning Need
As previously identified, members of FQHC patient populations have decreased health literacy resulting in noncompliance with follow-up appointments and poorer patient outcomes. However, a systematic review of 60 studies identified text messages as an effective intervention to improve patient compliance, thereby increasing patient outcomes (Hirshberg, Downes, & Srinivas, 2018). Text messages provide a low-cost, highly scalable intervention to improve patient follow-up (Arora et al., 2015). Additionally, few members of society do not have access to a mobile phone. Text messages sent seven days, and one day before scheduled appointments are effective intervals for improving patient follow-up (Arora et al., 2015). Potential obstacles for this intervention include patients changing their mobile numbers, having limited data usage, and the inability to know whether the message was received. However, using text messages to reinforce the need for compliance with follow-up care will address the learning deficit for this patient population. Additionally, orientating the office staff and then including them in the implementation of this intervention will address the staff’s learning need. Therefore, a PowerPoint presentation should be designed to orient the staff to this technology-based, evidence-based intervention.
Learning needs exist in all patient populations. Ongoing advances in technology are providing new methods for addressing these needs. By taking advantage of these technologies, evidence-based interventions can successfully be implemented in the practice setting.
Arora, S., Burner, E., Terp, S., Nok Lam, C., Nercisian, A., Bhatt, V., & Menchine, M. (2015). Improving attendance at post–emergency department follow‐up via automated text message appointment reminders: A randomized controlled trial. Academic Emergency Medicine, 22(1), 31-37.
Health Resources & Services Administration. (2018, May 8). Federally Qualified Health Centers. Retrieved from https://www.hrsa.gov/opa/eligibility-and-registration/health-centers/fqhc/index.html
Health Resources & Services Administration. (2019). 2018 Health Center Data. Retrieved from https://bphc.hrsa.gov/uds/datacenter.aspx?q=tall&year=2018&state=
Health Resources & Services Administration. (n.d.). HRSA Health Center Program. Retrieved October 1, 2019, from https://bphc.hrsa.gov/sites/default/files/bphc/about/healthcenter factsheet.pdf
Hirshberg, A., Downes, K., & Srinivas, S. (2018). Comparing standard office-based follow-up with text-based remote monitoring in the management of postpartum hypertension: A randomised clinical trial. BMJ Quality & Safety, 27(11), 871-877. doi:10.1136/bmjqs-2018-007837
Thompson, A. C., Thompson, M. O., Young, D. L., Lin, R. C., Sanislo, S. R., Moshfeghi, D. M., & Singh, K. (2015). Barriers to follow-up and strategies to improve adherence to appointments for care of chronic eye diseases. Investigative Ophthalmology & Visual Science, 56(8), 4324-4331. doi:10.1167/iovs.15-16444
Whitley, M. Y., Jones, E. M. V. W., Hansen, B. K., & Vora, J. (2019). The impact of self-monitoring blood glucose adherence on glycemic goal attainment in an indigent population, with pharmacy assistance. Pharmacy and Therapeutics, 44(9), 554. doi:10.43 21/s1885-642×2006000400006
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