Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization

Abstract

Background: Chronic spinal pain conditions affect millions of US adults and carry a high healthcare cost burden, both direct and indirect. Conservative interventions for spinal pain conditions, including chiropractic care, have been associated with lower healthcare costs and improvements in pain status in different clinical populations, including veterans. Little is currently known about predicting healthcare service utilization in the domain of conservative interventions for spinal pain conditions, including the frequency of use of chiropractic services. The purpose of this retrospective cohort study was to explore the use of supervised machine learning approaches to predicting one-year chiropractic service utilization by veterans receiving VA chiropractic care. Methods: We included 19,946 veterans who entered the Musculoskeletal Diagnosis Cohort between October 1, 2003 and September 30, 2013 and utilized VA chiropractic services within one year of cohort entry. The primary outcome was one-year chiropractic service utilization following index chiropractic visit, split into quartiles represented by the following classes: 1 visit, 2 to 3 visits, 4 to 6 visits, and 7 or greater visits. We compared the performance of four multiclass classification algorithms (gradient boosted classifier, stochastic gradient descent classifier, support vector classifier, and artificial neural network) in predicting visit quartile using 158 sociodemographic and clinical features. Results: The selected algorithms demonstrated poor prediction capabilities. Subset accuracy was 42.1% for the gradient boosted classifier, 38.6% for the stochastic gradient descent classifier, 41.4% for the support vector classifier, and 40.3% for the artificial neural network. The micro-averaged area under the precision-recall curve for each oneversus-rest classifier was 0.43 for the gradient boosted classifier, 0.38 for the stochastic gradient descent classifier, 0.43 for the support vector classifier, and 0.42 for the artificial neural network. Performance of each model yielded only a small positive shift in prediction probability (approximately 15%) compared to naïve classification.

Evaluating Rates of Chiropractic Use and Utilization by Patient Sex within the United States Veterans Health Administration: A Serial Cross-Sectional Analysis

“…Within our cohort, on average females received 4.9 visits per year and males 4.6 visits and median rates of utilization were 4.3 for females and 4.1 for males, respectively. This is approximately one annual visit higher on average than a previous VHA study, but similarly showed a pattern of higher overall utilization rates among female than male patients [21]. This same study also found that, when strati ed by number of visits, there were more female veterans in the 7 + visit quartile than any of the other three quartiles [21].…”

“…This is approximately one annual visit higher on average than a previous VHA study, but similarly showed a pattern of higher overall utilization rates among female than male patients [21]. This same study also found that, when strati ed by number of visits, there were more female veterans in the 7 + visit quartile than any of the other three quartiles [21].…”

“…There is a paucity of research regarding utilization rates -the number of visits per patient per year -for VHA chiropractic patients. A report of 19,946 veterans (F = 13.5%) over a ten-year period found the 75th percentile of one-year chiropractic service utilization to be 6 visits (range 1 to 73) [21]. A second VHA study of a single clinic reported the average number of visits for the 292 patients studied was 6.44 (SD = 4.65) in calendar year 2006 [22].…”

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

“…For the topic of health care utilization and costs, 24 articles were included in this review. Most were retrospective cohort studies, 75 76 77 78 79 80 81 82 83 84 85 86 87 while six used prospective cohort studies, 88 89 90 91 92 93 four used a cross-sectional design, 94 95 96 97 and one used a survey for primary data collection. 98 Most studies used the EHR and administrative databases to collect data but three used surveys, 87 96 98 two used public datasets, 78 95 one used mobile phone data, 97 one used images, 90 and one used data from social media.…”

The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review

“…Furthermore, AI integration is accelerating in the conventional medical field, and the introduction of AI in complementary and alternative medicine (CAM) is also being attempted (Coleman et al, 2020;Khan et al, 2021). According to the National Health Interview Survey (NHIS) conducted in the United States, 33.2% of adults and 11.6% of children respond they use CAM, and 30% of the medical payments ($14.7 billion) were paid to CAM (Barnes et al, 2004;Barnes et al, 2008).…”