Self-scheduling in a Large Multispecialty and Multisite Clinic: A Retrospective, Longitudinal Examination of Multiple Self-Scheduled Visit Types.

Background: Self-scheduling of medical visits is becoming available at many medical institutions. We aimed to examine the self-scheduled visit counts and rate of growth of self-scheduled visits in a multispecialty practice. Methods: For 85 weeks extending from January 1, 2022 through August 24, 2023, we examined self-scheduled visit counts for over 1500 self-scheduled visit types. We compared completed self-scheduled visit counts to all scheduled completed visit counts for the same visit types. We collected counts of the most frequently self-scheduled visit types for each week and examined the change over time. We also determined the proportion that each visit type was self-scheduled. Results: There were 20,769 699 completed visits during the course of the study that met the criteria for inclusion. Self-scheduled visits accounted for 4.0% of all completed visits (838 592/20,769 699). Over the 85-week span, self-scheduled visits rose from 3.0% to 5.3% of the total. There were 1887 unique visit types that were associated with completed visits. There were just 6 appointment visit types of the total 1887 self-scheduled visit types that accounted for 50.7% of the total 838 592 self-scheduled visits. Those 6 visit types were a lab blood test visit (19.5%, 163 K visits), two Family Medicine office visit types (13.0%, 109 K visits), a screening mammogram visit type (6.6%, 55 K visits), a scheduled express care visit type (6%, 50 K visits) and a COVID immunization visit type (5.7%, 48 K visits). Twenty-one visit types that were self-scheduled accounted for 75% of the total self-scheduled visits. Four seasonal visits, accounting for 10.6% of the total self-scheduled visits, were responsible for almost all the non-linear change in self-scheduling. Conclusion: Self-scheduling accounted for a small but growing percent of all outpatient scheduled visits in a multispecialty, multisite practice. A wide range of visit types can be successfully self-scheduled.

Self-scheduling Medical Visits in a Multispecialty, Multisite Medical Practice: Complexity, Challenges, and Successes.

Background: Self-scheduling of medical visits is becoming more common but the complexity of applying multiple requirements for self-scheduling has hampered implementation. Mayo Clinic implemented self-scheduling in 2019 and has been increasing its portfolio of self-schedulable visits since then. Our aim was to show measures quantifying the complexity associated with medical visit scheduling and to describe how opportunities and challenges of scheduling complexity apply in self-scheduling. Methods: We examined scheduled visits from January 1, 2022, through August 24, 2023. For seven visit categories, we counted all unique visit types that were scheduled, for both staff-scheduled and self-scheduled. We examined counts of self-scheduled visit types to identify those with highest uptake during the study period. Results: There were 9555 unique visit types associated with 20.8 M (million) completed visits. Self-scheduled visit types accounted for 4.0% (838,592/20,769,699) of the completed total visits. Of seven visit categories, self-scheduled established patient visits, testing visits, and procedure visits accounted for 93.5% (784,375/838,592) of all self-scheduled visits. Established patient visits in primary care (10 visit types) accounted for 273,007 (32.6%) of all self-scheduled visits. Testing visits (blood and urine testing, 2 visit types) accounted for 183,870 (21.9%) of all self-scheduled visits. Procedure visits for screening mammograms, bone mineral density, and immunizations (8 visit types) accounted for 147,358 (17.6%) of all self-scheduled visits. Conclusion: Large numbers of unique visit types comprise a major challenge for self-scheduling. Some visit types are more suitable for self-scheduling. Guideline-based procedure visits such as screening mammograms, bone mineral density exams, and immunizations are examples of visits that have high volumes and can be standardized for self-scheduling. Established patient visits and laboratory testing visits also can be standardized for self-scheduling. Despite the successes, there remain thousands of specific visit types that may need some staff-scheduler intervention to properly schedule.

Recommendation endpoints and safety of an online self-triage for depression symptoms.

INTRODUCTION: Online symptom checkers are a way to address patient concerns and potentially offload a burdened healthcare system. However, safety outcomes of self-triage are unknown, so we reviewed triage recommendations and outcomes of our institution's depression symptom checker. METHODS: We examined endpoint recommendations and follow-up encounters seven days afterward during 2 December 2021 to 13 December 2022. Patients with an emergency department visit or hospitalization within seven days of self-triaging had a manual review of the electronic health record to determine if the visit was related to depression, suicidal ideation, or suicide attempt. Charts were reviewed for deaths within seven days of self-triage. RESULTS: There were 287 unique encounters from 263 unique patients. In 86.1% (247/287), the endpoint was an instruction to call nurse triage; in 3.1% of encounters (9/287), instruction was to seek emergency care. Only 20.2% (58/287) followed the recommendations given. Of the 229 patients that did not follow the endpoint recommendations, 121 (52.8%) had some type of follow-up within seven days. Nearly 11% (31/287) were triaged to endpoints not requiring urgent contact and 9.1% (26/287) to an endpoint that would not need any healthcare team input. No patients died in the study period. CONCLUSIONS: Most patients did not follow the recommendations for follow-up care although ultimately most patients did receive care within seven days. Self-triage appears to appropriately sort patients with depressed mood to emergency care. On-line self-triaging tools for depression have the potential to safely offload some work from clinic personnel.

Hospitalization Risk Associated With Emergency Department Reasons for Visit and Patient Age: A Retrospective Evaluation of National Emergency Department Survey Data to Help Identify Potentially Avoidable Emergency Department Visits.

Background: Patients often present to emergency departments (EDs) with concerns that do not require emergency care. Self-triage and other interventions may help some patients decide whether they should be seen in the ED. Symptoms associated with low risk of hospitalization can be identified in national ED data and can inform the design of interventions to reduce avoidable ED visits. Methods: We used the National Hospital Ambulatory Medical Care Survey (NHAMCS) data from the United States National Health Care Statistics (NHCS) division of the Centers for Disease Control and Prevention (CDC). The ED datasets from 2011 through 2020 were combined. Primary reasons for ED visit and the binary field for hospital admission from the ED were used to estimate the proportion of ED patients admitted to the hospital for each reason for visit and age category. Results: There were 221,027 surveyed ED visits during the 10-year data collection with 736 different primary reasons for visit and 23,228 hospitalizations. There were 145 million estimated hospitalizations from 1.37 billion estimated ED visits (10.6%). Inclusion criteria for this study were reasons for visit which had at least 30 ED visits in the sample; there were 396 separate reasons for visit which met this criteria. Of these 396 reasons for visit, 97 had admission percentages less than 2% and another 52 had hospital admissions estimated between 2% and 4%. However, there was a significant increase in hospitalizations within many of the ED reasons for visit in older adults. Conclusion: Reasons for visit from national ED data can be ranked by hospitalization risk. Low-risk symptoms may help healthcare institutions identify potentially avoidable ED visits. Healthcare systems can use this information to help manage potentially avoidable ED visits with interventions designed to apply to their patient population and healthcare access.

Online Self-Triage of Ear or Hearing Concerns in a Patient Portal: Comparison of Subsequent Diagnoses and Hospitalizations to National Emergency Department and National Ambulatory Ear or Hearing Visits.

Background: Although online self-triage is easily accessible, little is known about the patients who use self-triage or their subsequent diagnoses. We compared ear/hearing self-triage subsequent diagnoses to ear/hearing visit diagnoses in emergency departments (ED) and ambulatory clinics across the United States. Methods: We compared International Classification of Diseases version 10 (ICD10) coded diagnoses following online self-triage for ear/hearing concerns with those from national ED and ambulatory clinic samples. We used data from the Centers for Disease Control (CDC) National Hospital Ambulatory Medical Care Survey (NHAMCS) and National Ambulatory Medical Care Survey (NAMCS) for comparison. Using matched ear/hearing diagnostic categories for those aged 1 and over, we compared self-triage diagnosis frequencies with national ED and ambulatory diagnosis frequencies. Results: Following ear/hearing self-triage, there were 1092 subsequent office visits with a primary diagnosis code. For five frequently diagnosed ear/hearing conditions (i.e., suppurative and nonsuppurative otitis media [OM], otalgia, otitis externa, and cerumen impaction), there was a strong correlation between diagnosis counts made following self-triage and estimated counts of national ED visit diagnoses (r = 0.94; CI 95% [0.37 to 0.99]; p = .016, adjusted r2 = 0.85). Seven diagnoses were available to compare with the national ambulatory sample; correlation was r = 0.79; CI 95% [0.08 to 0.97]; p = .037, adjusted r2 = 0.54. For ages 1 and over, estimated hospital admissions from the national ED visits for ear/hearing were 0.76%, CI 95% [0.28-2.1%]; estimated total national ear/hearing ED visits were 7.5 million (for 4 years, 2016 through 2019). Conclusion: The strong correlation of ear-related self-triage diagnoses with national ED diagnoses and the low hospitalization risk for these diagnoses suggests that there is an opportunity for self-triage of ear/hearing concerns to decrease ED visits for these symptoms.

Self-Triage Use, Subsequent Healthcare Utilization, and Diagnoses: A Retrospective Study of Process and Clinical Outcomes Following Self-Triage and Self-Scheduling for Ear or Hearing Symptoms.

Background: Self-triage is becoming more widespread, but little is known about the people who are using online self-triage tools and their outcomes. For self-triage researchers, there are significant barriers to capturing subsequent healthcare outcomes. Our integrated healthcare system was able to capture subsequent healthcare utilization of individuals who used self-triage integrated with self-scheduling of provider visits. Methods: We retrospectively examined healthcare utilization and diagnoses after patients had used self-triage and self-scheduling for ear or hearing symptoms. Outcomes and counts of office visits, telemedicine interactions, emergency department visits, and hospitalizations were captured. Diagnosis codes associated with subsequent provider visits were dichotomously categorized as being associated with ear or hearing concerns or not. Nonvisit care encounters of patient-initiated messages, nurse triage calls, and clinical communications were also captured. Results: For 2168 self-triage uses, we were able to capture subsequent healthcare encounters within 7 days of the self-triage for 80.5% (1745/2168). In subsequent 1092 office visits with diagnoses, 83.1% (891/1092) of the uses were associated with relevant ear, nose and throat diagnoses. Only 0.24% (4/1662) of patients with captured outcomes were associated with a hospitalization within 7 days. Self-triage resulted in a self-scheduled office visit in 7.2% (126/1745). Office visits resulting from a self-scheduled visit had significantly fewer combined non-visit care encounters per office visit (fewer combined nurse triage calls, patient messages, and clinical communication messages) than office visits that were not self-scheduled (-0.51; 95% CI, -0.72 to -0.29; P < .0001). Conclusion: In an appropriate healthcare setting, self-triage outcomes can be captured in a high percentage of uses to examine for safety, patient adherence to recommendations, and efficiency of self-triage. With the ear or hearing self-triage, most uses had subsequent visit diagnoses relevant to ear or hearing, so most patients appeared to be selecting the appropriate self-triage pathway for their symptoms.

Self-Scheduling Process Efficiency and Utilization of Online Self-Scheduling of Lab Tests: A Retrospective Analysis of Self-Scheduled Appointments for COVID Testing.

Introduction: The COVID 19 pandemic increased the need for rapid and accurate diagnostic testing for COVID. When testing became available, a systems response was needed to efficiently accommodate the high-volume flow of patients who needed testing. Self-scheduling of COVID testing was developed to help patients safely and efficiently schedule their COVID testing online or with a mobile app. Methods: We captured the counts of COVID test appointments, time patients spent in scheduling COVID test appointments, appointment lead times, and no-shows for COVID test appointments. For 17 months of self-scheduling, we retrospectively compared self-scheduling with the concurrent staff scheduling of COVID tests. Results: From November 2020 through March 2022 there were 619 104 scheduled appointments for COVID testing with 22% (136 252) being self-scheduled. For asymptomatic self-scheduled COVID tests, accounting for 10.3% (63 605/619 104) of total COVID tests scheduled, median time to self-schedule was 3.1 min, interquartile range (IQR) [2.4,4.7]. For symptomatic self-schedulers accounting for 11.7% (72 647/619 104) of total COVID tests scheduled, the median time to self-triage and self-schedule was 5.8 min, IQR[4.3,8.9]. Self-scheduled COVID appointments increased to 44% (42 387/97 086) of the total COVID appointments during the peak month of January 2022. Median appointment lead time for symptomatic self-scheduled COVID test appointments was 6.6 h compared to 2.9 h (P < .0001) for symptomatic staff scheduled appointments. However, adjusting for the 24% (32 194/135 252) that self-scheduled during hours when testing was unavailable, the median appointment lead time for symptomatic self-scheduled patients dropped to 3.6 h. No-shows were 2.5% for self-scheduled appointments compared to 3.0% no-shows that were staff scheduled (odds ratio 0.83, P < .0001). Conclusion: COVID testing was self-scheduled for a large percent of scheduled COVID tests, taking patients only a few minutes to complete. Self-scheduling use increased over time, associated with a decreasing use of staff scheduled appointments and lower no-shows.

Portal Message Language Use Prior to Suicide, Suicide Attempts, and Hospitalization for Depression.

Introduction: Previous research suggests patients may be willing to communicate serious psychiatric concerns through patient portals. Methods: Retrospective chart review of portal messages sent by patients who had an emergency department (ED) visit or hospitalization for depression, self-harm, or suicidality or had a completed suicide (cases) was reviewed for content that was suggestive of depression or self-harm and language indicating emotional distress. Comparison with a randomly selected group (controls) was performed. Results: During the study period 420 messages were sent by 149 patients within 30 days of death by suicide, ED visit, and/or hospitalization related to depression, suicidality, or suicide attempt. Thirteen patients died by suicide but only 23% (3 of 13) sent one or more portal messages within 30 days before their death. None mentioned thoughts of self-harm. There were 271 messages sent by patients who were hospitalized, 142 messages by those who presented to the ED, and 56 messages patients who attempted suicide. Patient messages from cases were more likely than messages from controls to convey a depressed mood (17.1% vs. 3.1%, odds ratio 6.5; 95% confidence interval 3.6-11.9, p < 0.0001), thoughts of suicide or self-harm (4.8% vs. 0% p < 0.0001), or have a distressed tone (24.0% vs. 1.7%, odds ratio 18.7; 95% confidence interval 8.6-41, p < 0.0001). Conclusions: Patient portal messages from patients with subsequent hospitalizations for depression and suicidality do report thoughts of depression, distress, and thoughts of self-harm. However, portal use before completed suicide was not helpful at identifying at-risk patients although total numbers were small.

The Effect of Automated Mammogram Orders Paired With Electronic Invitations to Self-schedule on Mammogram Scheduling Outcomes: Observational Cohort Comparison.

BACKGROUND: Screening mammography is recommended for the early detection of breast cancer. The processes for ordering screening mammography often rely on a health care provider order and a scheduler to arrange the time and location of breast imaging. Self-scheduling after automated ordering of screening mammograms may offer a more efficient and convenient way to schedule screening mammograms. OBJECTIVE: The aim of this study was to determine the use, outcomes, and efficiency of an automated mammogram ordering and invitation process paired with self-scheduling. METHODS: We examined appointment data from 12 months of scheduled mammogram appointments, starting in September 2019 when a web and mobile app self-scheduling process for screening mammograms was made available for the Mayo Clinic primary care practice. Patients registered to the Mayo Clinic Patient Online Services could view the schedules and book their mammogram appointment via the web or a mobile app. Self-scheduling required no telephone calls or staff appointment schedulers. We examined uptake (count and percentage of patients utilizing self-scheduling), number of appointment actions taken by self-schedulers and by those using staff schedulers, no-show outcomes, scheduling efficiency, and weekend and after-hours use of self-scheduling. RESULTS: For patients who were registered to patient online services and had screening mammogram appointment activity, 15.3% (14,387/93,901) used the web or mobile app to do either some mammogram self-scheduling or self-cancelling appointment actions. Approximately 24.4% (3285/13,454) of self-scheduling occurred after normal business hours/on weekends. Approximately 9.3% (8736/93,901) of the patients used self-scheduling/cancelling exclusively. For self-scheduled mammograms, there were 5.7% (536/9433) no-shows compared to 4.6% (3590/77,531) no-shows in staff-scheduled mammograms (unadjusted odds ratio 1.24, 95% CI 1.13-1.36; P<.001). The odds ratio of no-shows for self-scheduled mammograms to staff-scheduled mammograms decreased to 1.12 (95% CI 1.02-1.23; P=.02) when adjusted for age, race, and ethnicity. On average, since there were only 0.197 staff-scheduler actions for each finalized self-scheduled appointment, staff schedulers were rarely used to redo or "clean up" self-scheduled appointments. Exclusively self-scheduled appointments were significantly more efficient than staff-scheduled appointments. Self-schedulers experienced a single appointment step process (one and done) for 93.5% (7553/8079) of their finalized appointments; only 74.5% (52,804/70,839) of staff-scheduled finalized appointments had a similar one-step appointment process (P<.001). For staff-scheduled appointments, 25.5% (18,035/70,839) of the finalized appointments took multiple appointment steps. For finalized appointments that were exclusively self-scheduled, only 6.5% (526/8079) took multiple appointment steps. The staff-scheduled to self-scheduled odds ratio of taking multiple steps for a finalized screening mammogram appointment was 4.9 (95% CI 4.48-5.37; P<.001). CONCLUSIONS: Screening mammograms can be efficiently self-scheduled but may be associated with a slight increase in no-shows. Self-scheduling can decrease staff scheduler work and can be convenient for patients who want to manage their appointment scheduling activity after business hours or on weekends.

COVID-19 Care Clinic in a Medical Center: Lessons Learned.

OBJECTIVE: The purpose of this report is to describe the elements of a Covid-19 Care Clinic (CCC), patient demographics, and outcomes. METHODS: Descriptive statistics were used to describe demographics, clinical characteristics, and outcomes. This report is based on 4934 unique patients seen in the CCC who provided research authorization within a 10-month period of time (April 1, 2020-January 31, 2021). The CCC infection control processes consisted of a rooming process that mitigated SARS-COV-2 transmission, preparing examination rooms, using PPE by staff, in room lab drawing, and escorting services to minimize the time in clinic. RESULTS: Of the 4934 unique patients seen (age range newborn-102 years), 76.8% were tested for COVID-19. Of those tested, 11.8% were positive for SARS-CoV-2. Ninety-two percent of the patients with the reason for the visit documented had COVID-19 type symptoms. Cough, shortness of breath, and chest pain were the most common presenting symptom in those with COVID-19. At the time of the visit in the CCC, 5.8% of the patients were actively contagious. Thirty days after being seen in the CCC, 9.1% of the patients were seen in the emergency department (ED) and 0.2% died. During the 10-month period there were no known occupationally related COVID-19 infections. CONCLUSION: The COVID-19 Care Clinic provided face-to-face access for all ages with COVID-19 type symptoms. A minority of patients had COVID-19 who were seen in the clinic. The clinic provided an additional venue of care outside of the ED. The infectious control measures employed were highly effective in protecting the staff. Lessons learned allow for decentralization of COVID-19 symptom care to the primary care practices employing the infection control measures.

Impact of Web-Based Self-Scheduling on Finalization of Well-Child Appointments in a Primary Care Setting: Retrospective Comparison Study.

BACKGROUND: Web-booking of flights, hotels, and sports events has become commonplace in the travel and entertainment industry, but self-scheduling of health care appointments on the web is not yet widely used. An electronic health record that integrates appointment scheduling and patient web-based access to medical records creates an opportunity for patient self-scheduling. The Mayo Clinic developed and implemented a feature in its Patient Online Services (POS) web and mobile platform that allows software-managed self-scheduling of well-child visits. OBJECTIVE: This study aims to examine the use of a new self-scheduling appointment feature within POS in both web and mobile formats and determine the use characteristics, outcomes, and efficiency of self-scheduling compared with staff scheduling. METHODS: Within a primary care setting, we collected 13 months of all appointment activity for the well-child visit for children aged 2-12 years. As these specific appointment types are for minors, self-scheduling is performed by parents or other proxies. We compared the appointment actions of scheduling and cancelling for both self-scheduled and staff-scheduled appointments. The frequency in which patients were using self-scheduling outside of normal business hours was quantified, and we compared no-show outcomes of finalized appointments. RESULTS: Of the 1099 patients who performed any self-scheduling actions, 73.1% (803/1099) exclusively used self-scheduling and self-cancelling software. For those with access to self-scheduling (patients registered with the Mayo Clinic POS), 4.92% (1201/24,417) of all well-child appointment-scheduling actions were self-scheduled. Staff scheduling required more than a single appointment step (eg, schedule, cancel, reschedule) in 28.32% (3729/13,168) compared with only 6.93% (53/765) of self-scheduled appointments (P<.001). Self-scheduling appointment actions took place outside of regular business hours 29.5% (354/1201) of the time. No-shows accounted for 3.07% (28/912) of the self-scheduled finalized appointments compared with 4.12% (693/16,828) of staff-scheduled appointments, which is a nonsignificant difference (P=.12). Staff-scheduled finalized appointments (that allowed for scheduling appointments for more than 12 weeks in the future) revealed a potential demand of 11.15% (1876/16,828) for appointments with longer lead times. CONCLUSIONS: Self-scheduling can generate a significant number of finalized appointments, decreasing the need for staff scheduler time. We found that 29.5% (354/1201) of the self-scheduling activity took place outside of the usual staff scheduler hours, adding convenience value to the scheduling process. For exclusive self-schedulers, 93.1% (712/765) finalized the appointment in a single step. The no-show rates were not adversely affected by the self-scheduling.

Patient portal message characteristics and reported thoughts of self-harm and suicide: A retrospective cohort study.

INTRODUCTION: As use of electronic portal communication with healthcare teams increases, processes that effectively recognize messages that contain critical information are needed. This study aims to evaluate whether certain language and other characteristics of patient portal messages are associated with expressions of self-harm and suicidal ideation. METHODS: Using patient portal messages sent between 1 January 2013 and 30 June 2017, we searched for words and letter combinations 'suicid' (to identify words suicide and suicidal), 'depress' (for depression, depressed, depressing), 'harm himself' (or 'herself 'or 'myself'), 'hurt himself' ('herself' or 'myself'), 'kill', 'shoot', 'cutting', 'knife', 'gun', 'overdose', 'over dose' and 'jump'. RESULTS: Of 831,009 messages, 11,174 messages contained one or more search terms. We manually reviewed 7,736 messages for content expressing self-harm or suicidality. Of the reviewed messages, 3.2% indicated thoughts of self-harm or suicide and 2.2% of messages suggested active suicidality. Of those expressing any thoughts of self-harm or suicide, 13.4% mentioned a specific plan, 20% were passively suicidal. Messages indicating thoughts of self-harm and suicide were more common in patients who were unmarried, non-white and younger than 18 years. Factors significantly associated with thoughts of self-harm were messages addressed to psychiatry or containing the letter combinations 'suicide', 'die', 'depress' and 'harm/hurt my/her/himself'. DISCUSSION: Certain letter combinations and patient portal message characteristics may be associated with expressions of self-harm and suicide. These factors should be considered as we develop systems of effectively screening patient portal messages for critical clinical information.

A Retrospective Analysis of Provider-to-Patient Secure Messages: How Much Are They Increasing, Who Is Doing the Work, and Is the Work Happening After Hours?

BACKGROUND: Patient portal registration and the use of secure messaging are increasing. However, little is known about how the work of responding to and initiating patient messages is distributed among care team members and how these messages may affect work after hours. OBJECTIVE: This study aimed to examine the growth of secure messages and determine how the work of provider responses to patient-initiated secure messages and provider-initiated secure messages is distributed across care teams and across work and after-work hours. METHODS: We collected secure messages sent from providers from January 1, 2013, to March 15, 2018, at Mayo Clinic, Rochester, Minnesota, both in response to patient secure messages and provider-initiated secure messages. We examined counts of messages over time, how the work of responding to messages and initiating messages was distributed among health care workers, messages sent per provider, messages per unique patient, and when the work was completed (proportion of messages sent after standard work hours). RESULTS: Portal registration for patients having clinic visits increased from 33% to 62%, and increasingly more patients and providers were engaged in messaging. Provider message responses to individual patients increased significantly in both primary care and specialty practices. Message responses per specialty physician provider increased from 15 responses per provider per year to 53 responses per provider per year from 2013 to 2018, resulting in a 253% increase. Primary care physician message responses increased from 153 per provider per year to 322 from 2013 to 2018, resulting in a 110% increase. Physicians, nurse practitioners, physician assistants, and registered nurses, all contributed to the substantial increases in the number of messages sent. CONCLUSIONS: Provider-sent secure messages at a large health care institution have increased substantially since implementation of secure messaging between patients and providers. The effort of responding to and initiating messages to patients was distributed across multiple provider categories. The percentage of message responses occurring after hours showed little substantial change over time compared with the overall increase in message volume.

Is a switch to a different electronic health record associated with a change in patient satisfaction?

OBJECTIVE: Financial impacts associated with a switch to a different electronic health record (EHR) have been documented. Less attention has been focused on the patient response to an EHR switch. The Mayo Clinic was involved in an EHR switch that occurred at 6 different locations and with 4 different "go-live" dates. We sought to understand the relationship between patient satisfaction and the transition to a new EHR. MATERIALS AND METHODS: We used patient satisfaction data collected by Press Ganey from July 2016 through December 2019. Our patient satisfaction measure was the percent of patients responding "very good" (top box) to survey questions. Twenty-four survey questions were summarized by Press Ganey into 6 patient satisfaction domains. Piecewise linear regression was used to model patient satisfaction before and after the EHR switch dates. RESULTS: Significant drops in patient satisfaction were associated with the EHR switch. Patient satisfaction with access (ease of getting clinic on phone, ease of scheduling appointments, etc.) was most affected (range of 6 sites absolute decline: -3.4% to -8.8%; all significant at 99% confidence interval). Satisfaction with providers was least affected (range of 6 sites absolute decline: -0.5% to -2.8%; 4 of 6 sites significant at 99% confidence interval). After 9-15 months, patient satisfaction with access climbed back to pre-EHR switch levels. CONCLUSIONS: Patient satisfaction in several patient experience domains dropped significantly and stayed lower than pre-"go-live" for several months after a switch in EHR. Satisfaction with providers declined less than satisfaction with access.

Frequency of and Factors Associated with Care Partner Proxy Interaction with Health Care Teams Using Patient Portal Accounts.

Introduction: Proxies can communicate with health care teams through patient portals either by using proxy login credentials or a patient's login credentials. The frequency of proxies using patient login credentials is unknown. Methods: A random selection of 3,000 portal messages sent in through adult patients' own portal account was reviewed for indicators (referring to the patient in the third person) that someone other than the patient was using the patient portal account. Results: Of the reviewed 3,000 portal messages sent through patient portal accounts, 221 (7.4%) appeared to be sent in by a proxy, 2,512 (83.8%) appeared to have been sent in by the patient and for 266 (8.9%) portal messages reviewed it was unclear who sent in the message. There was no difference in mean age between patients who had proxy messages sent through patient portal accounts versus proxy portal accounts. Patients who had proxies send messages through patient accounts were more likely to be married and male. Out of 221 manually reviewed messages apparently sent by proxies through patient portal accounts there were 113 (51%) where the proxy included their name and 56 (25.3%) where they reported their relationship to the patient. During the study period, 0.7% of total messages on adult patients were sent through proxy accounts. Discussion: Proxies appear to use patient portal accounts much more frequently than proxy accounts to communicate with the health care team on adult patients; however, when using patient accounts they only identify themselves approximately half of the time.

What Can Apologies in the Electronic Health Record Tell Us About Health Care Quality, Processes, and Safety?

INTRODUCTION: Apologizing to patients is an encouraged practice, yet little is known about how and why providers apologize and what insights apologies could provide in improving quality and safety. OBJECTIVE: The aim of the study was to determine whether provider apologies in the electronic health record could identify patient safety concerns and opportunities for improvement. METHODS: After performing a free-text search, we randomly selected 100 clinical notes from 1685 available containing terminology related to apology. We categorized the reason for apology, presence and classification of medical error, level of patient harm, and practice improvement opportunities. We compared patient events discovered from apologies in the medical record to standard patient incident report logs. RESULTS: Of 100 randomly selected apologies, 37 were related to a delay in care, 14 to misunderstanding, 11 to access to care, and 8 to information technology. For apologies related to delay, the median delay was 6 days (mean = 8.9, range = 0-41). Twenty-four (65%) of the 37 delays were related to diagnostic testing.Medical errors were associated with 46 (46%) of the 100 apologies. Sixty-four (64%) of the 100 apologies were associated with actionable opportunities for improvement. These opportunities were classified into 37 discrete issues across 8 broad categories. When apology review was compared with standard incident report logs, 27 (73%) of the 37 discrete issues identified by patient apology review were not found in incident reporting; both methods identified similar rates of patient harm. CONCLUSIONS: Review of apologies in the electronic health record can identify patient safety concerns and improvement opportunities not apparent through standard incident reporting.

Patient Satisfaction With Providers: Do Patient Surveys Give Enough Information to Help Providers Improve Specific Behaviors.

BACKGROUND: Patient satisfaction surveys ask patients specific questions about provider behavior such as whether they were satisfied with the provider's instructions about medications or time spent with the patient. It's unclear how responses to these surveys can help providers focus on specific behaviors to improve. METHODS: In a primary care setting, we analyzed Press Ganey patient experience survey responses. We examined the 10 questions dealing with satisfaction specific to the care provider experience. We used the "Top Box" counts (counts of most favorable responses) and Top Box% (percentage of most favorable response) for categorical and continuous measures of patient satisfaction. RESULTS: For 12 consecutive months, 652 providers of 1014 accumulated at least 300 total responses from patients for the 10 provider-related questions. Only 8 of the 652 providers had significant differences (P < .05) in Top Box% for the 10 questions. Correlation of responses between the questions were between 0.86 and 0.96. Analysis of variance showed that 87% of the total variation in the Top Box% of the 10 questions was between providers and only 13% within providers. Factor analysis found no independent factors within the 10 questions (ie, a one factor model was sufficient; P < .0001). CONCLUSION: Patient survey questions appear to ask about specific provider behaviors that contribute to patient experience. However, the responses to 10 different questions are highly correlated and may not give providers or management enough statistically significant information to focus patient experience improvement efforts for individual providers.

Primary Care Provider Continuity Is Associated With Improved Preventive Service Ordering During Brief Visits for Acute Symptoms.

BACKGROUND: If a patient presents for an acute care visit and sees their assigned primary care provider (PCP), they may be more likely to receive preventive and other services than a patient not seeing their assigned PCP. METHODS: After exclusion of 2 visits with insufficient information, we reviewed 98 consecutive, outpatient internal medicine 15-minute acute care visits comparing patients seeing their assigned PCP with those seeing a non-PCP provider. The primary outcome, preventive service ordering, was measured in 2 ways: percentage of patient visits with any preventive service ordered and the total number of preventive services ordered as a proportion of all preventive service items due for each entire cohort. The secondary outcome of other work completed was assessed by comparing tests and consults ordered, and by counting the number of physical examination elements and discrete medical diagnoses documented. RESULTS: The PCPs were significantly more likely than non-PCPs to order any preventive service 45% versus 17% (P = .005; odds ratio [OR]: 4.16, 95% confidence interval [CI]: 1.45-12.0). The PCP cohort ordered a higher proportion of the total number of preventive services due compared with the non-PCP cohort (30% vs 11%; P = .002; OR: 3.4, CI: 1.5-7.7). The PCPs also addressed more medical diagnoses (2.3 vs 1.4; P = .008) and more frequently ordered tests outside the reason for that visit (40% vs 13%; P = .003; OR: 4.27, CI: 1.5-11.8). CONCLUSION: Patients seeing their assigned PCP in brief, acute visits have higher rates of preventive and other service ordering compared to those not seeing their assigned PCP.

Evaluating diverse electronic consultation programs with a common framework.

BACKGROUND: Electronic consultation is an emerging mode of specialty care delivery that allows primary care providers and their patients to obtain specialist expertise without an in-person visit. While studies of individual programs have demonstrated benefits related to timely access to specialty care, electronic consultation programs have not achieved widespread use in the United States. The lack of common evaluation metrics across health systems and concerns related to the generalizability of existing evaluation efforts may be hampering further growth. We sought to identify gaps in knowledge related to the implementation of electronic consultation programs and develop a set of shared evaluation measures to promote further diffusion. METHODS: Using a case study approach, we apply the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) and the Quadruple Aim frameworks of evaluation to examine electronic consultation implementation across diverse delivery systems. Data are from 4 early adopter healthcare delivery systems (San Francisco Health Network, Mayo Clinic, Veterans Administration, Champlain Local Health Integration Network) that represent varied organizational structures, care for different patient populations, and have well-established multi-specialty electronic consultation programs. Data sources include published and unpublished quantitative data from each electronic consultation database and qualitative data from systems' end-users. RESULTS: Organizational drivers of electronic consultation implementation were similar across the systems (challenges with timely and/or efficient access to specialty care), though unique system-level facilitators and barriers influenced reach, adoption and design. Effectiveness of implementation was consistent, with improved patient access to timely, perceived high-quality specialty expertise with few negative consequences, garnering high satisfaction among end-users. Data about patient-specific clinical outcomes are lacking, as are policies that provide guidance on the legal implications of electronic consultation and ideal remuneration strategies. CONCLUSION: A core set of effectiveness and implementation metrics rooted in the Quadruple Aim may promote data-driven improvements and further diffusion of successful electronic consultation programs.

Population health challenges in primary care: What are the unfinished tasks and who should do them?

BACKGROUND: There are numerous recommendations from expert sources that help guide primary care providers in cancer screening, infectious disease screening, metabolic screening, monitoring of drug levels, and chronic disease management. Little is known about the potential effort needed for a healthcare system to address these recommendations, or the patient effort needed to complete the recommendations. METHODS: For 73 recommended population healthcare items, we examined each of 28,742 patients in a primary care internal medicine practice to determine whether they were up-to-date on recommended screening, immunizations, counseling, and chronic disease management goals. We used a rule-based software tool that queries the medical record for diagnoses, dates, laboratory values, pathology reports, and other information used in creating the individualized recommendations. We counted the number of uncompleted recommendations by age groups and examined the healthcare staff needed to address the recommendations and the potential patient effort needed to complete the recommendations. RESULTS: For the 28,742 patients, there were 127,273 uncompleted recommendations identified for population health management (mean recommendations per patient 4.36, standard deviation of 2.65, range of 0-17 recommendations per patient). The age group with the most incomplete recommendations was age of 50-65 years with 5.5 recommendations per patient. The 18-35 years age group had the fewest incomplete recommendations with 2.6 per patient. Across all age groups, initiation of these recommendations required high-level input (physician, nurse practitioner, or physician's assistant) in 28%. To completely adhere to recommended services, a 1000-patient cross-section cohort would require a total of 464 procedures and 1956 lab tests. CONCLUSION: Providers and patients face a daunting number of tasks necessary to meet guideline-generated recommendations. We will need new approaches to address the burgeoning numbers of uncompleted recommendations.