Why Military Personnel Fail to Keep Medical Appointments

Challenges With Engaging Military Stakeholders for Clinical Research at the Point of Care in the U.S. Military Health System

The DoD has a specific mission that creates unique challenges for the conduct of clinical research. These unique challenges include (1) the fact that medical readiness is the number one priority, (2) understanding the role of military culture, and (3) understanding the highly transient flow of operations. Appropriate engagement with key stakeholders at the point of care, where research activities are executed, can mean the difference between success and failure. These key stakeholders include the beneficiaries of the study intervention (patients), clinicians delivering the care, and the military and clinic leadership of both. Challenges to recruitment into research studies include military training, temporary duty, and deployments that can disrupt availability for participation. Seeking medical care is still stigmatized in some military settings. Uniformed personnel, including clinicians, patients, and leaders, are constantly changing, often relocating every 2-4 years, limiting their ability to support clinical trials in this setting which often take 5-7 years to plan and execute. When relevant stakeholders are constantly changing, keeping them engaged becomes an enduring priority. Military leaders are driven by the ability to meet the demands of the assigned mission (readiness). Command endorsement and support are critical for service members to participate in stakeholder engagement panels or clinical trials offering novel treatments. To translate science into relevant practice within the Military Health System, early engagement with key stakeholders at the point of care and addressing mission-relevant factors is critical for success.

Characterising the nationwide burden and predictors of unkept outpatient appointments in the National Health Service in England: A cohort study using a machine learning approach

BACKGROUND

Unkept outpatient hospital appointments cost the National Health Service £1 billion each year. Given the associated costs and morbidity of unkept appointments, this is an issue requiring urgent attention. We aimed to determine rates of unkept outpatient clinic appointments across hospital trusts in the England. In addition, we aimed to examine the predictors of unkept outpatient clinic appointments across specialties at Imperial College Healthcare NHS Trust (ICHT). Our final aim was to train machine learning models to determine the effectiveness of a potential intervention in reducing unkept appointments.

METHODS AND FINDINGS

UK Hospital Episode Statistics outpatient data from 2016 to 2018 were used for this study. Machine learning models were trained to determine predictors of unkept appointments and their relative importance. These models were gradient boosting machines. In 2017-2018 there were approximately 85 million outpatient appointments, with an unkept appointment rate of 5.7%. Within ICHT, there were almost 1 million appointments, with an unkept appointment rate of 11.2%. Hepatology had the highest rate of unkept appointments (17%), and medical oncology had the lowest (6%). The most important predictors of unkept appointments included the recency (25%) and frequency (13%) of previous unkept appointments and age at appointment (10%). A sensitivity of 0.287 was calculated overall for specialties with at least 10,000 appointments in 2016-2017 (after data cleaning). This suggests that 28.7% of patients who do miss their appointment would be successfully targeted if the top 10% least likely to attend received an intervention. As a result, an intervention targeting the top 10% of likely non-attenders, in the full population of patients, would be able to capture 28.7% of unkept appointments if successful. Study limitations include that some unkept appointments may have been missed from the analysis because recording of unkept appointments is not mandatory in England. Furthermore, results here are based on a single trust in England, hence may not be generalisable to other locations.

CONCLUSIONS

Unkept appointments remain an ongoing concern for healthcare systems internationally. Using machine learning, we can identify those most likely to miss their appointment and implement more targeted interventions to reduce unkept appointment rates.

Predictive Factors for Patients' Failure to Show for Initial Outpatient Physical Therapist Evaluation

OBJECTIVE

The objective of this study was to examine primary factors that may predict patients' failure to show at initial physical therapist evaluation in an orthopedic and sports outpatient setting.

METHODS

A retrospective analysis of patients' demographic data for physical therapist evaluations between January 2013 and April 2015 was performed. A binary logistic regression model was used to evaluate the odds of a no-show at evaluation. Demographic variables of age, employment status, days waited for the appointment, payer source, and distance traveled to the clinic were analyzed. Independent variables were considered significant if the 95% CIs of the odds ratios (ORs) did not include 1.0.

RESULTS

A total of 6971 patients were included in the final analysis, with 10% (n = 698) of the scheduled patients having a no-show event for their initial evaluation. The following factors increased the odds of patients having a no-show event: days to appointment (OR = 1.058; 95% CI = 1.042-1.074), unemployment status (OR = 1.96; 95% CI = 1.41-2.73), unknown employment status (OR = 3.22; 95% CI = 1.12-8.69), Medicaid insurance (OR = 4.87; 95% CI = 3.43-6.93), Medicare insurance (OR = 2.22; 95% CI = 1.10-4.49), unknown payer source (OR = 262.84; 95% CI = 188.72-366.08), and distance traveled 8 or more kilometers (OR = 1.31; 95% CI = 1.01-1.70). Female sex (OR = 0.73; 95% CI = 0.57-0.95) and age 40 years or older (OR = 0.44; 95% CI = 0.33-0.60) decreased the odds of a no-show event.

CONCLUSIONS

Results from this study indicate there may be some demographic factors that are predictive of patients failing to attend their first physical therapist visit.

IMPACT

Understanding the predictive factors and identifying potential opportunities for improvements in scheduling processes might help decrease the number of patients failing to show for their initial physical therapy appointment, with the ultimate goal of positively influencing patient outcomes.

Demographic factors associated with loss to follow up in the management of chronic otitis media: case-control study

OBJECTIVE

The likelihood of a patient attending regular follow up can affect decision making when planning and performing tympanomastoid surgery. This study investigated whether demographic factors were associated with loss to follow up.

METHODS

A database of patients who had been investigated and treated for chronic otitis media was searched. Patients lost to follow up and a matching sample of patients who were formally discharged were identified. The demographic factors of age, sex and postcode were compared between the two groups. The information collected was also used to provide measures of deprivation.

RESULTS

Fifty patients in each group were identified. Patients lost to follow up were significantly younger than patients formally discharged (p < 0.02), and were more likely to live in an area of education and training deprivation (p < 0.05).

CONCLUSION

Younger patient age, and living in an area of education and training deprivation, are associated with a higher incidence of loss to follow up.

No-Show to Primary Care Appointments

Background: Missed primary care appointments lead to poor disease control and later presentation to care. No-show rates are higher in clinics caring for underserved populations and may contribute to poorer health outcomes in this group. The objective of this study was to determine who were the patients not showing to primary care appointments and their reasons to no-show. Methods: A retrospective study was conducted at a community health center serving a predominantly Latino, immigrant, low-income population. Adult patients >18 years old who did not show to primary care appointments during a 5-month period were called by a bilingual (English and Spanish) patient service coordinator. The patients’ reported reason for missing the appointment was documented. Two-sided t test of proportions was used to compare demographic characteristics of the patients that showed to their appointments to patients that did not. Results: Of 7508 scheduled appointments, 5604 were included in the analysis and 927 (16.5%) no-showed. There were 735 (79%) calls made to the patients who missed their appointments and 273 (37%) were reached. The 2 most common reasons for missing an appointment were forgetting (n = 97, 35.5%) and miscommunication (n = 86, 31.5%). When compared with patients who came to their appointments, patients who no-showed were younger ( P < .0001), more likely to be black ( P = .0423) or Hispanic ( P = .0001), and to have Medicaid ( P < .0001). Conclusions: No-show rates interfere with quality primary care. Interventions designed to target reasons for no-show are needed to help reduce the no-show rate, improve access and decrease health disparities in underserved patient populations.