Pecina, Jennifer

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.

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: 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.

Objective The objective of this research paper is to compare antibiotic treatment, follow-up rates, and types of follow-up encounters among eVisits, phone calls, and in-person encounters for pediatric conjunctivitis. Study design A retrospective chart review of pediatric patients evaluated for conjunctivitis between May 1, 2016 and May 1, 2017, was performed. A total of 101 eVisits, 202 in-person retail clinic visits, and 202 nurse phone calls for conjunctivitis were manually reviewed for outcomes. Exclusion criteria included previous encounter for conjunctivitis in the past 14 days, treatment with an oral antibiotic at the initial encounter, or patient outside Minnesota at the time of encounter. Comparison among the three encounter types with regard to follow-up rates, follow-up encounter type within 14 days of initial evaluation, and prescribing rates was performed. Results Patients completing non-face-to-face encounters were significantly more likely to have follow-up care (34.6% and 45.5%) than those who had a face-to-face visit at the retail clinic (7.4%), p ≤ 0.0001. Patients initially evaluated by eVisit were more likely to have follow-up at the retail clinic while patients initially evaluated by phone call were more likely to have follow-up in their primary care office. Treatment rates with antibiotics were significantly higher in phone call encounters (41.6%) than in eVisits (25.7%) or face-to-face encounters (19.8%), p < 0.0001. Conclusions Non-face-to-face visits have significantly higher rates of follow-up when compared to face-to-face encounters. Antibiotic prescribing is greater with phone call triage encounters; however, there was no significant difference in antibiotic prescribing rates between eVisits and face-to-face visits. Follow-up type varied according to site of initial encounter.

Introduction: E-consultations are asynchronous text-based consultations between providers which can facilitate patient access to timely specialty care. In contrast to traditional face-to-face consults, conveying and completing recommendations of the specialist is the responsibility of the referring provider. This presents a new workflow for primary care providers who have multiple options (face-to-face, telephone, letter, secure message) to communicate the e-consultation recommendations. This study examines how primary care providers are managing this new workflow. Methods: We performed a retrospective random sampling of e-consultations with individual medical record review and classified e-consultations by type of recommendation, how recommendations were communicated to patients, and whether recommendations were carried out. Results: We randomly selected 220 e-consultations in 13 different specialties for review. In all, 85% of e-consultations contained recommendations for referring providers. Recommendations on medication(s) were most common (35%) followed by recommendations on ordering laboratory tests (29%). In all, 25% of the time e-consultants gave multiple possible courses of action for referring providers to choose from. Patient notification of recommendations was found for 192 (87%) of e-consultations with providers performing the notification 63% of the time and nursing staff performing the notification 37% of the time. The communication back to the patients included communication via nurse telephone calls (37%), provider telephone calls (23%), secure messages (24%), face-to-face visits (11%), and by written correspondence (5%). Discussion: Managing recommendations from e-consultations results in a new workflow for primary care providers. Healthcare institutions that utilize e-consults should be aware of this new workflow. Further study is needed to determine best practices for this task that is now increasing in primary care.

AbstractRationale, aims and objectivesHospital readmission within 30 days of discharge occurs in almost 20% of US Medicare patients and may be a marker of poor quality inpatient care, ineffective hospital to home transitions, or disease severity. Within a patient centered medical home, care transition interventions may only be practical from cost and staffing perspectives if targeted at patients with the greatest risk of readmission. Various scoring algorithms attempt to predict patients at risk for 30‐day readmission, but head‐to‐head comparison of performance is lacking. Compare published scoring algorithms which use generally available electronic medical record data on the same set of hospitalized primary care patients.MethodsThe LACE index, the LACE+ index, the HOSPITAL score, and the readmission risk score were computed on a consecutive cohort of 26,278 hospital admissions. Classifier performance was assessed by plotting receiver operating characteristic curves comparing the computed score with the actual outcome of death or readmission within 30 days. Statistical significance of differences in performance was assessed using bootstrapping techniques.ResultsCorrect readmission classification on this cohort was moderate with the following c‐statistics: Readmission risk score 0.666; LACE 0.680; LACE+ 0.662; and HOSPITAL 0.675. There was no statistically significant difference in performance between classifiers.ConclusionsLogistic regression based classifiers yield only moderate performance when utilized to predict 30‐day readmissions. The task is difficult due to the variety of underlying causes for readmission, nonlinearity, and the arbitrary time period of concern. More sophisticated classification techniques may be necessary to increase performance and allow patient centered medical homes to effectively focus efforts to reduce readmissions.

Introduction Under certain circumstances, e-consultations can substitute for a face-to-face consultation. A basic requirement for a successful e-consultation is that the e-consultant has access to important medical history and exam findings along with laboratory and imaging results. Knowing just what information the specialist needs to complete an e-consultation is a major challenge. This paper examines differences between specialties in their need for past information from laboratory, imaging and clinical notes. Methods This is a retrospective study of patients who had an internal e-consultation performed at an academic medical centre. We reviewed a random sample of e-consultations that occurred in the first half of 2013 for the indication for the e-consultation and whether the e-consultant reviewed data in the medical record that was older than one year to perform the e-consultation. Results Out of 3008 total e-consultations we reviewed 360 (12%) randomly selected e-consultations from 12 specialties. Questions on management (35.8%), image results (27.2%) and laboratory results (25%) were the three most common indications for e-consultation. E-consultants reviewed medical records in existence more than one year prior to the e-consultation 146 (40.6%) of the time with e-consultants in the specialties of endocrinology, haematology and rheumatology, reviewing records older than one year more than half the time. Labs (20.3%), office notes (20%) and imaging (17.8%) were the types of medical data older than one year that were reviewed the most frequently overall. Discussion Management questions appear to be the most common reason for e-consultation. E-consultants frequently reviewed historical medical data that is older than one year at the time of the e-consultation, especially in endocrinology, haematology and rheumatology specialties. Practices engaging in e-consultations that require transfer of data may want to include longer time frames of historical information for those specialties.

Introduction E-consultations are asynchronous, text-based consultations. The specialist e-consultant answers clinical questions in a similar way to a standard consultation but the questions and answers are sent electronically. The e-consultant has access to some or all of the medical record but does not have contact with the patient. Although e-consultations are meant to substitute for face-to-face (F2F) consultations, a significant proportion of e-consultations are converted to F2F consultations. Methods We examined e-consultation content from a sample of e-consultations that had subsequent F2F visits in the same specialty as the e-consultation within 28 days of the e-consultation. Results Out of 5115 e-consultations, there were a total of 547 (10.7%) early F2F conversions. One hundred and fifty-one e-consultations with subsequent early F2F conversions were reviewed in eight specialties. In 64% of the F2F conversions, specialists recommended the F2F consultations. In 75% there were complex diagnostic or treatment considerations. In only 1% was there a sense of medical urgency or a stated need for physical examination. Discussion E-consultations convert to F2F consultations primarily at the request of the specialist. Diagnostic and treatment complexity appear to be the main reasons. We found little evidence that patients decided independently to get a F2F visit or that specialists needed a F2F visit to perform a physical examination. Although e-consultations might not be a complete substitute for F2F consultations, they may serve as an entry level consultation that could be supplemented by a video consultation as needed for cases with more diagnostic and treatment complexity.