Effect of Telemedicine Use on Medical Spending and Health Care Utilization: A Machine Learning Approach
Introduction: This study analyzes the effect of telemedicine use on healthcare utilization and medical spending for patients with chronic mental illness. Methods: Using the IBM MarketScan Research database from 2009 to 2018, this study examined the timing of users’ first telemedicine use and identif...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | AJPM Focus |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773065423000640 |
| _version_ | 1797732873836429312 |
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| author | Ayesha Jamal, PhD |
| author_facet | Ayesha Jamal, PhD |
| author_sort | Ayesha Jamal, PhD |
| collection | DOAJ |
| description | Introduction: This study analyzes the effect of telemedicine use on healthcare utilization and medical spending for patients with chronic mental illness. Methods: Using the IBM MarketScan Research database from 2009 to 2018, this study examined the timing of users’ first telemedicine use and identified similar periods for non-users by using random forest and random forest proximity matching. A difference-in-differences approach, which tests whether there are differences in the study outcomes before and after the actual/predicted first use among the treated group (users) compared with the control group (non-users), was then used to assess the impact of telemedicine. Analyses were done in 2021. Results: Comparing users with non-users after matching suggested that telemedicine use both increases the number of overall outpatient visits (0.461; 95% CI=0.280, 0.642; p<0.001) related to psychotherapy and evaluation and management services, and decreases the number of in-person visits (0.280; 95% CI= −0.446, −0.114; p=0.001) for patients with chronic mental health diagnoses. Total medical spending was not significantly affected. Additionally, no evidence was found of telemedicine use being associated with an increased probability of an emergency department visit or hospitalization. Conclusions: The study findings suggest that telemedicine use is associated with an increase in outpatient care utilization for patients with chronic mental health diagnoses. No substantive changes in medical spending, the probability of an emergency department visit, or the probability of hospitalization were noted. Results provide insights into the effect of telemedicine use on spending and healthcare utilization for patients with chronic mental illness. These findings may inform research to guide future telemedicine policies and interventions. |
| first_indexed | 2024-03-12T12:19:49Z |
| format | Article |
| id | doaj.art-9aa4f0acbce84e349a667883f51670b1 |
| institution | Directory Open Access Journal |
| issn | 2773-0654 |
| language | English |
| last_indexed | 2024-03-12T12:19:49Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | AJPM Focus |
| spelling | doaj.art-9aa4f0acbce84e349a667883f51670b12023-08-30T05:55:09ZengElsevierAJPM Focus2773-06542023-09-0123100127Effect of Telemedicine Use on Medical Spending and Health Care Utilization: A Machine Learning ApproachAyesha Jamal, PhD0Address correspondence to: Ayesha Jamal, PhD, Department of Economics and Finance, Murray State University, 307H Business Building, Murray KY 42071.; Arthur J. Bauernfeind College of Business, Department of Economics and Finance, Murray State University Murray, KentuckyIntroduction: This study analyzes the effect of telemedicine use on healthcare utilization and medical spending for patients with chronic mental illness. Methods: Using the IBM MarketScan Research database from 2009 to 2018, this study examined the timing of users’ first telemedicine use and identified similar periods for non-users by using random forest and random forest proximity matching. A difference-in-differences approach, which tests whether there are differences in the study outcomes before and after the actual/predicted first use among the treated group (users) compared with the control group (non-users), was then used to assess the impact of telemedicine. Analyses were done in 2021. Results: Comparing users with non-users after matching suggested that telemedicine use both increases the number of overall outpatient visits (0.461; 95% CI=0.280, 0.642; p<0.001) related to psychotherapy and evaluation and management services, and decreases the number of in-person visits (0.280; 95% CI= −0.446, −0.114; p=0.001) for patients with chronic mental health diagnoses. Total medical spending was not significantly affected. Additionally, no evidence was found of telemedicine use being associated with an increased probability of an emergency department visit or hospitalization. Conclusions: The study findings suggest that telemedicine use is associated with an increase in outpatient care utilization for patients with chronic mental health diagnoses. No substantive changes in medical spending, the probability of an emergency department visit, or the probability of hospitalization were noted. Results provide insights into the effect of telemedicine use on spending and healthcare utilization for patients with chronic mental illness. These findings may inform research to guide future telemedicine policies and interventions.http://www.sciencedirect.com/science/article/pii/S2773065423000640Telemedicinechronic mental illnesshealthcare utilizationmedical spendingmachine learningrandom forest proximity matching |
| spellingShingle | Ayesha Jamal, PhD Effect of Telemedicine Use on Medical Spending and Health Care Utilization: A Machine Learning Approach AJPM Focus Telemedicine chronic mental illness healthcare utilization medical spending machine learning random forest proximity matching |
| title | Effect of Telemedicine Use on Medical Spending and Health Care Utilization: A Machine Learning Approach |
| title_full | Effect of Telemedicine Use on Medical Spending and Health Care Utilization: A Machine Learning Approach |
| title_fullStr | Effect of Telemedicine Use on Medical Spending and Health Care Utilization: A Machine Learning Approach |
| title_full_unstemmed | Effect of Telemedicine Use on Medical Spending and Health Care Utilization: A Machine Learning Approach |
| title_short | Effect of Telemedicine Use on Medical Spending and Health Care Utilization: A Machine Learning Approach |
| title_sort | effect of telemedicine use on medical spending and health care utilization a machine learning approach |
| topic | Telemedicine chronic mental illness healthcare utilization medical spending machine learning random forest proximity matching |
| url | http://www.sciencedirect.com/science/article/pii/S2773065423000640 |
| work_keys_str_mv | AT ayeshajamalphd effectoftelemedicineuseonmedicalspendingandhealthcareutilizationamachinelearningapproach |