New Tools, New Challenges: Navigating the Complexities of Digital Healthcare Delivery

Abstract

Emerging digital tools – such as telemedicine visits, remote monitoring, and patient portal messages – hold immense promise for improving healthcare access, quality, and efficiency. At the same time, they also introduce new challenges for healthcare organizations and policymakers. My dissertation sheds light on new dynamics brought about by three forms of digital care, providing practical guidance to managers on how to navigate them and policymakers on how to incentivize and enable effective use. In Chapter 1, titled “From Rooms to Zooms: The Hidden Costs of Hybrid Work in Primary Care”, my co-authors and I examine hybrid primary care practices that offer both in-person and telemedicine care. My findings highlight new frictions that arise when virtual visits are incorporated into still predominately in-person clinic schedules. Intermixing the two modalities can lead to costly “modality switch” transitions that can negatively impact subsequent visits. Telemedicine visits following an in-person visit often see delayed starts; patients are 75% more likely to abandon the visit before being seen, and the visits that do occur are 25% less likely to begin on time. These disruptions also result in less comprehensive visits and a higher likelihood of after-hours work. Dedicated telemedicine-only blocks in provider schedules help avoid these costly transitions but can also lead to reduced capacity utilization when there is insufficient demand for telemedicine visits in that time window. Indeed, we find that telemedicine-only slots see a 10% lower booking rate relative to similar slots without such restrictions. Telemedicine visits are often framed as a useful tool for improving patient care access. However, we show that, depending on how they are incorporated into hybrid schedules, they can lead to negative care experiences, chaotic clinic days, and ironically even reductions in patient access. Our findings also demonstrate the tradeoffs of dedicated telemedicine blocks and highlight potential changes to managerial practices and clinical workflows to improve performance of hybrid practices. In Chapter 2, titled “Practice-level Effects of Remote Physiologic Monitoring Adoption”, my co-authors and I leverage a 100% sample of Traditional Medicare claims data to study the practice level impacts of RPM adoption. Use of remote physiologic monitoring (RPM), the remote transmission of patient physiologic measures (e.g., blood pressure) to care teams, has grown rapidly in recent years. For practices, establishing an RPM program can increase revenue and improve patient care, but may also require substantial reorganization within the practice. No prior work has quantified the impact of RPM on practices. Using our Medicare claims dataset, we identified 754 primary care practices that began billing for RPM from 2019-2021. We find that, after these practices adopted RPM, Medicare revenue increased by 20.1% relative to similar matched non-adopting practices. This was driven by RPM billing as well as more outpatient visits and care management. While adopting practices had a 3.0% increase in their number of billing providers, the increase in revenue was predominantly driven by increased activity per provider. Adoption of RPM and resulting increases in visits for patients receiving RPM did not seem to come at the expense of other patients. Our results suggest that RPM holds promise as a tool for strengthening primary care and improving chronic disease management but also has the potential to substantially increase Medicare costs. In Chapter 3, titled “The Doctor Won’t See You Now: Examining Drivers of Care Team Response to Patient Portal Messages”, my co-authors and I investigate the drivers of provider engagement with patient portal messages. Prior work has shown that when patients from historically disadvantaged groups (e.g., racial and ethnic minorities, those with lower socioeconomic status) send messages to their care teams, they are less likely to receive responses from physicians, seemingly driven by lower prioritization in the message triaging process. In this study we leverage natural language processing (NLP) tools to analyze the text of patient portal messages from a large academic health system. Our goal is to understand what drives these differences in care team response, enabling us to separate three potential mechanisms: differences in message content and the underlying request of the message (e.g., medication question, referral request), differences in the way the messages are written, and non-clinical bias. We find that, while message content is a significant predictor of care team response, it cannot explain observed differences across demographic groups. On the other hand, the way the message is written – including writing style characteristics such as length and formality – accounts for nearly half of the observed differences. Our findings identify a clear mechanism underlying disparities in care team response, highlighting avenues for mitigating them and deepening our understanding of care disparities more broadly.