A Simple Way to Identify Patients Who Need Tech Support for Telemedicine
A function embedded in Johns Hopkins Medicine’s electronic-health-record system automatically identifies patients likely to need technical assistance so either someone from central IT support or a member of the clinical support or front desk teams can reach out to them before their visit. It gives patients individual scores based on the following risk factors: whether they have an account in the health system’s online patient portal, have completed an e-check-in process in the previous seven days, and have had a video visit in the last three months.
As telemedicine has quickly become a significant part of ambulatory clinical care, frontline providers and staff have struggled to adapt to one new support role that had not been part of their job descriptions: providing patients with technical assistance. A simple tool developed by Johns Hopkins Medicine — whose hospitals and clinics in Maryland, metropolitan Washington, D.C., and Florida serve more than 750,000 patients a year — can help.
It automatically identifies patients likely to need technical assistance so either someone from a centralized IT support team or a member of the clinical support or front desk teams can reach out to them before their visit. This approach can help make telemedicine more equitable and ease the extraordinary burden that the pandemic has imposed on care providers’ support staffs.
The Need for Targeted Support
Like other institutions across the country, Johns Hopkins Medicine experienced an exponential increase in the volume of telemedicine visits during the Covid-19 pandemic, and providers as well as members of our front desk and clinical support teams struggled to help patients navigate this new type of care. Our traditional IT support teams couldn’t adapt quickly enough. They continued to function in a passive way: They offered support to patients who called a help line for technical assistance but did not proactively reach out to them.
In order to help patients with new workflows, clinical and front desk support staff often generally called each patient before telemedicine visits to help make sure they were ready at their appointment time. However, these staff members told us that this it was difficult to take on this extra technical support work in addition to handling in-person patients visits and extra safety measures implemented to protect patients and staff from Covid-19.
From patients’ and families’ feedback, we learned that the phone-call-based support we were offering to walk patients through the process of getting ready for their visit needed to be tailored: Some patients required more help while others felt comfortable with the process and found the extra phone calls disruptive and unnecessary.
A Quick and Easy Way to Target Telemedicine Support
This information prompted us, together with colleagues in health IT and ambulatory operations, to develop an automated “video visit technical risk score” tool in our electronic health record (EHR) system to identify patients who would require technical assistance prior to their visits. It has the following components:
- The score ranges from 0 to 4, with 0 representing the lowest risk that a video visit would be unsuccessful and 4 representing the highest risk it would be.
- The score can be added as a column to be displayed on a schedule template and is color-coded based on the level of risk (0=green, 1-2=yellow, 3-4=red).
- The score is based on the presence of any of the following risk factors: two points for the patient not having an active account in MyChart, our online patient portal; one point for the patient not having completed our eCheck-in process in the previous seven days; and one point for either the patient not having had a video visit appointment in the past three months or the patient having had a telephone visit in the last three months and no video visits.
- The score is automatically calculated based on stored EHR data and displayed as a column that can be added to a provider’s or clinic staff member’s schedule views.
When we developed the risk score, approximately 15% to 20% of patients fell into the highest-risk categories (scores of three or four).
Implementing the Risk Score
The tool can be used by either a central IT support team or by frontline clinical and front desk staff so they can proactively reach out to patients in need of assistance. Our health system has employed both. As part of an iterative improvement pilot project, a central IT team has supported patients at three specially selected ambulatory clinics (two specialty care, one primary care) that had been identified as having struggled to help patients get ready for video visits. Beginning seven days before a scheduled visit, the central team reached out to patients via text messages that said: “Johns Hopkins Medicine: Setup for Video Visits can be challenging. Call XXX-XXX-XXXX anytime (24×7) for assistance in getting ready.”
In Phase 1 of this pilot (text-only outreach), text messages were manually sent to patients who had agreed to receive text message reminders and had a risk score of two or greater seven days, three days, and one day before their schedule video visit. Text messages were sent to 384 out of 766 patients (49%) whose cell phone numbers were in our EHR. Out of these patients, seven out of 384 (2%) returned a call to the central team for support.
In Phase 2 of the pilot (text + phone outreach), the text on the day before the appointment was replaced by a telephone call. With this change, 44 out of 98 (45% of patients) were successfully contacted by telephone in advance of a scheduled telemedicine visit. Since we found it challenging to contact patients for set-up before the time of their appointment, we plan to develop and implement processes to support patients at the time of a scheduled visit rather than in advance,
Outside of this pilot program, most of our clinic sites have chosen to continue to rely on their clinical support and front desk staff to support telemedicine visits. From what we have learned anecdotally, using the risk score has improved the efficiency of these teams.
Health systems leverage EHR data routinely to highlight which patients may need special attention during a visit (e.g., those who are due for vaccines or need an interpreter). Harnessing EHR data to identify patients likely to experience difficulties in accessing video visits is another important step in tapping the potential of these systems to provide a more individualized approach and make the best use of health care systems’ resources.
The authors would like to thank a number of their team members and colleagues at Johns Hopkins Medicine for their hard work in developing the processes discussed in this article. They include Deanna Hanisch, vice president of health information technology; Eric Brown, director of health Information technology; Cindy Diaz, Epic Systems development manager; Steve Klapper, Epic lead application coordinator; and Kathy Sapitowicz, telemedicine project lead.