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.

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.

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.

Telephone triage utilization among patients with limited English proficiency.

BACKGROUND: Communication between patients with limited English proficiency (LEP) and telephone triage services has not been previously explored. The purpose of this study was to determine the utilization characteristics of a primary care triage call center by patients with LEP. METHODS: This was a retrospective cohort study of the utilization of a computer-aided, nurse-led telephone triage system by English proficiency status of patients empaneled to a large primary care practice network in the Midwest United States. Interpreter Services (IS) need was used as a proxy for LEP. RESULTS: Call volumes between the 587 adult patients with LEP and an age-frequency matched cohort of English-Proficient (EP) patients were similar. Calls from patients with LEP were longer and more often made by a surrogate. Patients with LEP received recommendations for higher acuity care more frequently (49.4% versus 39.0%; P < 0.0004), and disagreed with recommendations more frequently (30.1% versus 20.9%; P = 0.0004). These associations remained after adjustment for comorbidities. Patients with LEP were also less likely to follow recommendations (60.9% versus 69.4%; P = 0.0029), even after adjusting for confounders (adjusted odds ratio [AOR] = 0.65; 95% confidence interval [CI], 0.49, 0.85; P < 0.001). CONCLUSION: Patients with LEP who utilized a computer-aided, nurse-led telephone triage system were more likely to receive recommendations for higher acuity care compared to EP patients. They were also less likely to agree with, or follow, recommendations given. Additional research is needed to better understand how telephone triage can better serve patients with LEP.

Clinician time used for decision making: a best case workflow study using cardiovascular risk assessments and Ask Mayo Expert algorithmic care process models.

BACKGROUND: Risk calculation is increasingly used in lipid management, congestive heart failure, and atrial fibrillation. The risk scores are then used for decisions about statin use, anticoagulation, and implantable defibrillator use. Calculating risks for patients and making decisions based on these risks is often done at the point of care and is an additional time burden for clinicians that can be decreased by automating the tasks and using clinical decision-making support. METHODS: Using Morae Recorder software, we timed 30 healthcare providers tasked with calculating the overall risk of cardiovascular events, sudden death in heart failure, and thrombotic event risk in atrial fibrillation. Risk calculators used were the American College of Cardiology Atherosclerotic Cardiovascular Disease risk calculator (AHA-ASCVD risk), Seattle Heart Failure Model (SHFM risk), and CHA2DS2VASc. We also timed the 30 providers using Ask Mayo Expert care process models for lipid management, heart failure management, and atrial fibrillation management based on the calculated risk scores. We used the Mayo Clinic primary care panel to estimate time for calculating an entire panel risk. RESULTS: Mean provider times to complete the CHA2DS2VASc, AHA-ASCVD risk, and SHFM were 36, 45, and 171 s respectively. For decision making about atrial fibrillation, lipids, and heart failure, the mean times (including risk calculations) were 85, 110, and 347 s respectively. CONCLUSION: Even under best case circumstances, providers take a significant amount of time to complete risk assessments. For a complete panel of patients this can lead to hours of time required to make decisions about prescribing statins, use of anticoagulation, and medications for heart failure. Informatics solutions are needed to capture data in the medical record and serve up automatically calculated risk assessments to physicians and other providers at the point of care.

Patient question set proliferation: scope and informatics challenges of patient question set management in a large multispecialty practice with case examples pertaining to tobacco use, menopause, and Urology and Orthopedics specialties.

BACKGROUND: Health care institutions have patient question sets that can expand over time. For a multispecialty group, each specialty might have multiple question sets. As a result, question set governance can be challenging. Knowledge of the counts, variability and repetition of questions in a multispecialty practice can help institutions understand the challenges of question set proliferation. METHODS: We analyzed patient-facing question sets that were subject to institutional governance and those that were not. We examined question variability and number of repetitious questions for a simulated episode of care. In addition to examining general patient question sets, we used specific examples of tobacco questions, questions from two specialty areas, and questions to menopausal women. RESULTS: In our analysis, there were approximately 269 institutionally governed patient question sets with a mean of 74 questions per set accounting for an estimated 20,000 governed questions. Sampling from selected specialties revealed that 50 % of patient question sets were not institutionally governed. We found over 650 tobacco-related questions in use, many with only slight variations. A simulated use case for a menopausal woman revealed potentially over 200 repeated questions. CONCLUSIONS: A group practice with multiple specialties can have a large volume of patient questions that are not centrally developed, stored or governed. This results in a lack of standardization and coordination. Patients may be given multiple repeated questions throughout the course of their care, and providers lack standardized question sets to help construct valid patient phenotypes. Even with the implementation of a single electronic health record, medical practices may still have a health information management gap in the ability to create, store and share patient-generated health information that is meaningful to both patients and physicians.

Assessment and improvement of HIV screening rates in a Midwest primary care practice using an electronic clinical decision support system: a quality improvement study.

BACKGROUND: Universal human immunodeficiency virus (HIV) screening remains low in many clinical practices despite published guidelines recommending screening for all patients between ages 13-65. Electronic clinical decision support tools have improved screening rates for many chronic diseases. We designed a quality improvement project to improve the rate of universal HIV screening of adult patients in a Midwest primary care practice using a clinical decision support tool. METHODS: We conducted this quality improvement project in Rochester, Minnesota from January 1, 2014 to December 31, 2014. Baseline primary care practice HIV screening data were acquired from January 1, 2014 to April 30, 2014. We surveyed providers and educated them about current CDC recommended screening guidelines. We then added an HIV screening alert to an existing electronic clinical decision support tool and post-intervention HIV screening rates were obtained from May 1, 2014 to December 31, 2014. The primary quality outcome being assessed was change in universal HIV screening rates. RESULTS: Twelve thousand five hundred ninety-six unique patients were eligible for HIV screening in 2014; 327 were screened for HIV. 6,070 and 6,526 patients were seen before and after the intervention, respectively. 1.80 % of eligible patients and 3.34 % of eligible patients were screened prior to and after the intervention, respectively (difference of -1.54 % [-2.1 %, -0.99 %], p < 0.0001); OR 1.89 (1.50, 2.38). Prior to the intervention, African Americans were more likely to have been screened for HIV (OR 3.86 (2.22, 6.71; p < 0.001) than Whites, but this effect decreased significantly after the intervention (OR 1.90 (1.12, 3.21; p = 0.03). CONCLUSIONS: These data showed that an electronic alert almost doubled the rates of universal HIV screening by primary care providers in a Midwestern practice and reduced racial disparities, but there is still substantial room for improvement in universal screening practices. Opportunities for universal HIV screening remain abundant, as many providers either do not understand the importance of screening average risk patients or do not remember to discuss it. Alerts to remind providers of current guidelines and help identify screening opportunities can be helpful.

Apple HealthKit and Health App: Patient Uptake and Barriers in Primary Care.

BACKGROUND: The Apple (Cupertino, CA) HealthKit is a new telemonitoring platform that promises to make it easier for patients and healthcare institutions to collect, transmit, and store data from devices that monitor common conditions such as diabetes, hypertension, and asthma. To assess the potential use for this platform in primary care, we need to know how many Apple Healthkit users there are and if they have conditions that could benefit from telemonitoring. MATERIALS AND METHODS: We examined patients in the Mayo Clinic primary care practice in Rochester, MN, who registered to connect to their Mayo Clinic medical record with Apple HealthKit. We used the primary care registry to identify users with chronic conditions of diabetes, hypertension, asthma, and depression. We also examined users for recent measurements of blood pressure, glucose, hemoglobin A1C, and cholesterol. RESULTS: Of 98,151 patients there were 503 registrants of HealthKit. There were 95 (19%) who had hypertension, 37 (7.4%) who had diabetes, 125 (25%) who had depression, and 56 (11%) who had asthma. Overall, there were 245 (49%) who had readily telemonitorable conditions. CONCLUSIONS: Almost half of primary care Apple HealthKit registrants have conditions that could benefit from telemonitoring. This pre-installed telemonitoring platform, available on every new iPhone(®) (Apple), can be used to monitor a significant number of primary care patients. However, it also has continued provider and informatics barriers that need to be addressed.

Telemonitoring Blood Pressure by Secure Message on a Patient Portal: Use, Content, and Outcomes.

INTRODUCTION: Secure messages on a patient portal allow patients to asynchronously communicate with their healthcare teams. Patients can use this mode of communication to transmit data such as home blood pressure (BP) measurements. MATERIALS AND METHODS: In this retrospective study, we examined 52,373 secure messages for content related to home BP monitoring. Text searches of the messages were followed by manual message review to identify BP-related messages. Two physicians independently reviewed a sample of these messages and the provider responses. RESULTS: Of 19,545 total message users, there were 4,412 message users with a diagnosis of hypertension and 365 who sent BP-related messages. Of the 52,373 secure messages, 624 messages (1.2%) contained information about home BP. Providers responded to messages with a change in medication dose or a prescription in 17%. When new medications were recommended, providers needed more pharmacy information in 53%. Messages contained a concern about high BP in 27% and concern about low BP in 8.5%. BP data in patient messages only attained American Heart Association-endorsed measurement criteria in 7% of messages. CONCLUSIONS: Patient-generated secure messages with BP data often result in message responses from providers for a BP medication dose change or a new prescription. Despite its increasing use, BP management by secure message has significant limitations and might be better served by BP virtual visits (e-visits) containing specific data requirements such as an average BP value from at least 12 readings and a preferred pharmacy for a prescription.

Clinical decision support improves quality of telephone triage documentation--an analysis of triage documentation before and after computerized clinical decision support.

BACKGROUND: Clinical decision support (CDS) has been shown to be effective in improving medical safety and quality but there is little information on how telephone triage benefits from CDS. The aim of our study was to compare triage documentation quality associated with the use of a clinical decision support tool, ExpertRN©. METHODS: We examined 50 triage documents before and after a CDS tool was used in nursing triage. To control for the effects of CDS training we had an additional control group of triage documents created by nurses who were trained in the CDS tool, but who did not use it in selected notes. The CDS intervention cohort of triage notes was compared to both the pre-CDS notes and the CDS trained (but not using CDS) cohort. Cohorts were compared using the documentation standards of the American Academy of Ambulatory Care Nursing (AAACN). We also compared triage note content (documentation of associated positive and negative features relating to the symptoms, self-care instructions, and warning signs to watch for), and documentation defects pertinent to triage safety. RESULTS: Three of five AAACN documentation standards were significantly improved with CDS. There was a mean of 36.7 symptom features documented in triage notes for the CDS group but only 10.7 symptom features in the pre-CDS cohort (p < 0.0001) and 10.2 for the cohort that was CDS-trained but not using CDS (p < 0.0001). The difference between the mean of 10.2 symptom features documented in the pre-CDS and the mean of 10.7 symptom features documented in the CDS-trained but not using was not statistically significant (p = 0.68). CONCLUSIONS: CDS significantly improves triage note documentation quality. CDS-aided triage notes had significantly more information about symptoms, warning signs and self-care. The changes in triage documentation appeared to be the result of the CDS alone and not due to any CDS training that came with the CDS intervention. Although this study shows that CDS can improve documentation, further study is needed to determine if it results in improved care.

Telemedicine barriers associated with regional quality measures.

Telemedicine practitioners are familiar with multiple barriers to delivering care at a distance. Licensing and reimbursement barriers are well known and are being addressed at national and state levels by the American Telemedicine Association. Another telemedicine barrier comes in the form of quality measures for diabetes. Minnesota medical practices are currently being compared on the proportion of their patients with diabetes who have attained goals for blood pressure, low-density lipoprotein cholesterol, and hemoglobin A1C. The quality measure for blood pressure specifically excludes measurements taken by the patient, thus precluding blood pressure telemonitoring as a way to meet the blood pressure goal. To counter this barrier, advocacy in telemedicine is needed so that telemonitoring as a data collection tool is included in quality measures.

Impact of patient portal secure messages and electronic visits on adult primary care office visits.

INTRODUCTION: Secure messages and electronic visits ("e-visits") through patient portals provide patients with alternatives to face-to-face appointments, telephone contact, letters, and e-mails. Limited information exists on how portal messaging impacts face-to-face visits in primary care. MATERIALS AND METHODS: We conducted a retrospective cohort study of 2,357 primary care patients who used electronic messaging (both secure messages and e-visits) on a patient portal. Face-to-face appointment frequencies (visits/year) of each patient were calculated before and after the first message in a matched-pairs analysis. We analyzed visit frequencies with and without adjustments for a first message surge in visits, and we examined subgroups of high message utilizers and long-term users. RESULTS: Primary care patients who sent at least one message (secure message or e-visit) had a mean of 2.43 (standard deviation [SD] 2.3) annual face-to-face visits before the first message and 2.47 (SD 2.8) after, a nonsignificant difference (p=0.45). After adjustment for a first message surge in visits, no significant visit frequency differences were observed (mean, 2.35 annual visits per patient both before and after first message; p=0.93). Subgroup analysis also showed no significant change in visit frequency for patients with higher message utilization or for those who had used the messaging feature longer. CONCLUSIONS: No significant change in face-to-face visit frequency was observed following implementation of portal messaging. Secure messaging and e-visits through a patient portal may not result in a change of adult primary care face-to-face visits.

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.

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.

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.

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.

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.

Should you search the Internet for information about your acute symptom?

OBJECTIVE: To determine if symptom-related Web sites give sufficient information for users to seek urgent care when warranted. MATERIALS AND METHODS: We reviewed 120 Web sites (15 sites for each of eight acute symptoms). Symptom-related sites were identified with Google, Yahoo!®, and Bing™ searches and focused on potentially hazardous symptoms such as chest pain, shortness of breath, abdominal pain, and syncope. We reviewed each symptom-related site for the presence of critical symptom indicators (key symptom characteristics and associated factors) that triage the user to urgent care. RESULTS: Of the 120 sites reviewed, 41 (33%) contained no critical symptom indicators. No site contained a complete set of critical symptom indicators. Overall, out of the 1,020 total critical symptoms searched for in the sites, we only found 329 (32%). When present, critical symptom indicators were found on the top half of the first page of the site in only 34%. Specific recommendations for further care were absent in 42% of the cases where critical symptom indicators were identified. CONCLUSIONS: Symptom-related sites ranked highly by major search engines lack much of the information needed to make a decision about whether a symptom needs urgent attention. When present, this information is usually not located where users can rapidly access it and often lacks prescriptive guidance for users to seek care. Until more sites contain at least minimal triage advice, relying on an Internet search to help determine the urgency of a symptom could be risky.

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.