Impact of Telehealth and Process Virtualization on Healthcare Utilization

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Abstract

Telehealth has emerged as a tool to improve patient access by virtualizing healthcare services, particularly during the COVID-19 pandemic. However, concerns have been raised that telehealth may actually increase healthcare spending by leading to new types of utilization. Our research provides empirical evidence that this concern is unfounded based on a state-wide study of patient visit-level data of telehealth use in 58 hospitals in Maryland from 2012 to 2021. On average, telehealth use can reduce future outpatient visits by 13.6% within 30 days after a telehealth visit, leading to a cost reduction of $239. The benefits of telehealth are most apparent for diseases with high potential for process virtualization, such as mental health, skin disorders, metabolic, and musculoskeletal diseases. Although telehealth has a substitution effect on future healthcare utilization, this effect is not observed among rural patients who use telehealth as a gateway to utilize more primary care and specialist services. Our findings suggest that policymakers should promote the use of telehealth in a value-based healthcare environment by providing monetary incentives to expand telehealth use among patients and providers, and expand the scope of telehealth services to include consultation with specialists especially among rural patients.

Abstract

Technological advancements and the COVID-19 pandemic have catapulted process virtualization across many industries, including healthcare, where telehealth has enabled significant digital transformation of care delivery. Although telehealth has been proposed as a potential solution to improve access to care and restrain runaway healthcare costs, it can increase spending if telehealth use leads to new types of resource utilization. Drawing on the lens of process virtualization theory, we study the impact of telehealth on healthcare utilization by examining visit-level patient data of telehealth use in facilitating e-visits with healthcare providers. On average, a telehealth visit reduces the number of future outpatient visits by 13.6% (or 0.15 visits), equal to a reduction of $239 in total cost within 30 days after the visit. Our results suggest that the benefits of telehealth use are observed primarily among diseases with high virtualization potential. Specifically, patients with mental health, skin, metabolic, and musculoskeletal diseases exhibit a significant reduction of 0.21 outpatient visits per quarter (an equivalent cost reduction of $179) when they are treated via telehealth, suggesting a substitution effect with respect to traditional clinic visits. Our research identifies the boundary conditions that determine the nuanced impact of telehealth on care utilization and shows that its effectiveness depends on the process virtualization potential of different diseases. Our findings have several practical and theoretical implications for fostering telehealth use in a value-based healthcare environment, especially for diseases with high virtualization potential where telehealth use should be promoted to bend the cost curve.

History: Rajiv Kohli, Senior Editor; Wenjing (Wendy) Duan, Associate Editor.

Funding: I. R. Bardhan thanks the Foster Parker Centennial Professorship and the Dean’s Research Excellence Grant at the McCombs School of Business for generous financial support.

Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2023.1220 .

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Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

Hude Quan, Vijaya Sundararajan, Patricia Halfon … (2005)

Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.