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. 2022 Jan 11;24(1):e17273.
doi: 10.2196/17273.

Characterizing Patient-Clinician Communication in Secure Medical Messages: Retrospective Study

Affiliations

Characterizing Patient-Clinician Communication in Secure Medical Messages: Retrospective Study

Ming Huang et al. J Med Internet Res. .

Abstract

Background: Patient-clinician secure messaging is an important function in patient portals and enables patients and clinicians to communicate on a wide spectrum of issues in a timely manner. With its growing adoption and patient engagement, it is time to comprehensively study the secure messages and user behaviors in order to improve patient-centered care.

Objective: The aim of this paper was to analyze the secure messages sent by patients and clinicians in a large multispecialty health system at Mayo Clinic, Rochester.

Methods: We performed message-based, sender-based, and thread-based analyses of more than 5 million secure messages between 2010 and 2017. We summarized the message volumes, patient and clinician population sizes, message counts per patient or clinician, as well as the trends of message volumes and user counts over the years. In addition, we calculated the time distribution of clinician-sent messages to understand their workloads at different times of a day. We also analyzed the time delay in clinician responses to patient messages to assess their communication efficiency and the back-and-forth rounds to estimate the communication complexity.

Results: During 2010-2017, the patient portal at Mayo Clinic, Rochester experienced a significant growth in terms of the count of patient users and the total number of secure messages sent by patients and clinicians. Three clinician categories, namely "physician-primary care," "registered nurse-specialty," and "physician-specialty," bore the majority of message volume increase. The patient portal also demonstrated growing trends in message counts per patient and clinician. The "nurse practitioner or physician assistant-primary care" and "physician-primary care" categories had the heaviest per-clinician workload each year. Most messages by the clinicians were sent from 7 AM to 5 PM during a day. Yet, between 5 PM and 7 PM, the physicians sent 7.0% (95,785/1,377,006) of their daily messages, and the nurse practitioner or physician assistant sent 5.4% (22,121/408,526) of their daily messages. The clinicians replied to 72.2% (1,272,069/1,761,739) patient messages within 1 day and 90.6% (1,595,702/1,761,739) within 3 days. In 95.1% (1,499,316/1,576,205) of the message threads, the patients communicated with their clinicians back and forth for no more than 4 rounds.

Conclusions: Our study found steady increases in patient adoption of the secure messaging system and the average workload per clinician over 8 years. However, most clinicians responded timely to meet the patients' needs. Our study also revealed differential patient-clinician communication patterns across different practice roles and care settings. These findings suggest opportunities for care teams to optimize messaging tasks and to balance the workload for optimal efficiency.

Keywords: message round; patient portal; patient-clinician communication; response time; secure message; workload.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Distribution of message count per patient or clinician (A and D), total number of patient-sent or clinician-sent messages and count of unique patients or clinicians by year (B and E), and box-whisker graph of message count per patient or clinician by year (C and F).
Figure 2
Figure 2
Total number of clinician-sent messages (A-C), the number of unique clinician senders (D-F), and median of message count per clinician (G-I) in each clinician category over years. NP: nurse practitioner; PA: physician assistant; RN: registered nurse.
Figure 3
Figure 3
Distribution of clinician messaging time in a day. Each color block represents the percentage of messages sent in the corresponding time slice during a day. NP: nurse practitioner; PA: physician assistant; RN: registered nurse.
Figure 4
Figure 4
Distribution (left) and box-whisker graph (right) of time delay in the clinician responses to patient messages.
Figure 5
Figure 5
Distribution (left), percentage (middle), and timespan (right) of message threads in terms of back-and-forth rounds in the message threads.

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