North, Frederick

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.

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.

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.

Abstract 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.

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.

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.

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.

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.

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.