Patient–Provider Communication: Does Electronic Messaging Reduce Incoming Telephone Calls?

Dexter, Eve; Fields, Scott; Rdesinski, Rebecca E.; Sachdeva, Bhavaya; Yamashita, Daisuke; Marino, Miguel

doi:10.3122/jabfm.2016.05.150371

Cited by 25 publications

(17 citation statements)

References 11 publications

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“…Patients’ office visit frequency was estimated to have remained similar by most respondents, and this perception is likely to be correct. Other studies led to an increase of both e-messaging and telephone encounters between patients and provider [ 36 , 37 ], whereas in a study in the USA, patients actually turned to their portals after visits, and portal use did not lead to an increase in primary care visits [ 38 ]. We expect that with more experience with the full range of possibilities a patient web portal has to offer, the workload may ultimately decrease as patients will start to use the portal for substitution of care.…”

Section: Discussionmentioning

confidence: 99%

Diabetes care providers’ opinions and working methods after four years of experience with a diabetes patient web portal; a survey among health care providers in general practices and an outpatient clinic

et al. 2018

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BackgroundTo gain insight into the opinions and working methods of diabetes care providers after using a diabetes web portal for 4 years in order to understand the role of the provider in patients’ web portal use.MethodsSurvey among physicians and nurses from general practices and an outpatient clinic, correlated with data from the common web portal.ResultsOne hundred twenty-eight questionnaires were analysed (response rate 56.6%). Responders’ mean age was 46.2 ± 9.8 years and 43.8% were physicians. The majority was of opinion that the portal improves patients’ diabetes knowledge (90.6%) and quality of care (72.7%). Although uploading glucose diary (93.6%) and patient access to laboratory and clinical notes (91.2 and 71.0%) were considered important, these features were recommended to patients in only 71.8 and 19.5% respectively. 64.8% declared they informed their patients about the portal and 45.3% handed-out the information leaflet and website address. The portal was especially recommended to type 1 diabetes patients (78.3%); those on insulin (84.3%) and patients aged< 65 years (72.4%). Few found it timesaving (21.9%). Diabetes care providers’ opinions were not associated with patients’ portal use.ConclusionsProviders are positive about patients web portals but still not recommend or encourage the use to all patients. There seems room for improvement in their working methods.Electronic supplementary materialThe online version of this article (10.1186/s12875-018-0781-y) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Diabetes care providers’ opinions and working methods after four years of experience with a diabetes patient web portal; a survey among health care providers in general practices and an outpatient clinic

et al. 2018

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“…[2–9] The increasing burden of secure messaging has been demonstrated in multiple settings with providers having a few messages per week to multiple messages per day within a few years after patient portal implementation. [10–13] This increasing burden can affect staffing and workflows of a clinic and health care providers. [14] Being able to determine the contents of these messages in an automated fashion could potentially mitigate this burden.…”

Section: Introductionmentioning

confidence: 99%

A comparison of rule-based and machine learning approaches for classifying patient portal messages

Cronin

Fabbri

Denny

et al. 2017

International Journal of Medical Informatics

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Objective Secure messaging through patient portals is an increasingly popular way that consumers interact with healthcare providers. The increasing burden of secure messaging can affect clinic staffing and workflows. Manual management of portal messages is costly and time consuming. Automated classification of portal messages could potentially expedite message triage and delivery of care. Materials and Methods We developed automated patient portal message classifiers with rule-based and machine learning techniques using bag of words and natural language processing (NLP) approaches. To evaluate classifier performance, we used a gold standard of 3253 portal messages manually categorized using a taxonomy of communication types (i.e., main categories of informational, medical, logistical, social, and other communications, and subcategories including prescriptions, appointments, problems, tests, follow-up, contact information, and acknowledgement). We evaluated our classifiers’ accuracies in identifying individual communication types within portal messages with area under the receiver-operator curve (AUC). Portal messages often contain more than one type of communication. To predict all communication types within single messages, we used the Jaccard Index. We extracted the variables of importance for the random forest classifiers. Results The best performing approaches to classification for the major communication types were: logistic regression for medical communications (AUC: 0.899); basic (rule-based) for informational communications (AUC: 0.842); and random forests for social communications and logistical communications (AUCs: 0.875 and 0.925, respectively). The best performing classification approach of classifiers for individual communication subtypes was random forests for Logistical-Contact Information (AUC: 0.963). The Jaccard Indices by approach were: basic classifier, Jaccard Index: 0.674; Naïve Bayes, Jaccard Index: 0.799; random forests, Jaccard Index: 0.859; and logistic regression, Jaccard Index: 0.861. For medical communications, the most predictive variables were NLP concepts (e.g. Temporal_Concept, which maps to ‘morning’, ‘evening’ and Idea_or_Concept which maps to ‘appointment’ and ‘refill’). For logistical communications, the most predictive variables contained similar numbers of NLP variables and words (e.g. Telephone mapping to ‘phone’, ‘insurance’). For social and informational communications, the most predictive variables were words (e.g. social: ‘thanks’, ‘much’, informational: ‘question’, ‘mean’). Conclusions This study applies automated classification methods to the content of patient portal messages and evaluates the application of NLP techniques on consumer communications in patient portal messages. We demonstrated that random forest and logistic regression approaches accurately classified the content of portal messages, although the best approach to classification varied by communication type. Words were the most predictive variables for classification of most communication types...

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“…21,22 Further study of the time costs, impact on professional satisfaction, and quality outcomes of asynchronous care is warranted.…”

Section: Asynchronous Patient Carementioning

confidence: 99%

Tethered to the EHR: Primary Care Physician Workload Assessment Using EHR Event Log Data and Time-Motion Observations

Beasley²,

et al. 2017

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PURPOSE Primary care physicians spend nearly 2 hours on electronic health record (EHR) tasks per hour of direct patient care. Demand for non-face-to-face care, such as communication through a patient portal and administrative tasks, is increasing and contributing to burnout. The goal of this study was to assess time allocated by primary care physicians within the EHR as indicated by EHR userevent log data, both during clinic hours (defined as 8:00 am to 6:00 pm Monday through Friday) and outside clinic hours. METHODSWe conducted a retrospective cohort study of 142 family medicine physicians in a single system in southern Wisconsin. All Epic (Epic Systems Corporation) EHR interactions were captured from "event logging" records over a 3-year period for both direct patient care and non-face-to-face activities, and were validated by direct observation. EHR events were assigned to 1 of 15 EHR task categories and allocated to either during or after clinic hours.RESULTS Clinicians spent 355 minutes (5.9 hours) of an 11.4-hour workday in the EHR per weekday per 1.0 clinical full-time equivalent: 269 minutes (4.5 hours) during clinic hours and 86 minutes (1.4 hours) after clinic hours. Clerical and administrative tasks including documentation, order entry, billing and coding, and system security accounted for nearly one-half of the total EHR time (157 minutes, 44.2%). Inbox management accounted for another 85 minutes (23.7%).CONCLUSIONS Primary care physicians spend more than one-half of their workday, nearly 6 hours, interacting with the EHR during and after clinic hours. EHR event logs can identify areas of EHR-related work that could be delegated, thus reducing workload, improving professional satisfaction, and decreasing burnout. Direct time-motion observations validated EHR-event log data as a reliable source of information regarding clinician time allocation. Ann Fam Med 2017;15:419-426. https://doi.org/10.1370/afm.2121. INTRODUCTIONP rimary care has become increasingly complex, 1 with electronic health record (EHR) systems adding to the complexity. Our patients expect same-day access for face-to-face care during clinic hours and rapid responses to telephone calls, patient portal messages, laboratory result inquiries, and prescription renewal requests both during and after clinic hours. This concurrent face-to-face (synchronous) and non-faceto-face (asynchronous) care, combined with administrative and regulatory work (prior authorizations, billing and coding, performance measurement) results in considerable strain on the primary care team. It is imperative to understand factors contributing to workload and identify practical solutions to these challenges.US physicians spend numerous hours daily interacting with EHR systems, contributing to work-life imbalance, dissatisfaction, high rates of attrition, and a burnout rate exceeding 50%. entry (CPOE), inbox management, patient portals, and a redistribution of tasks previously performed by clinical staff to clinicians has led to more work that is not direct fac...

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Patient–Provider Communication: Does Electronic Messaging Reduce Incoming Telephone Calls?

Cited by 25 publications

References 11 publications

Diabetes care providers’ opinions and working methods after four years of experience with a diabetes patient web portal; a survey among health care providers in general practices and an outpatient clinic

Diabetes care providers’ opinions and working methods after four years of experience with a diabetes patient web portal; a survey among health care providers in general practices and an outpatient clinic

A comparison of rule-based and machine learning approaches for classifying patient portal messages

Tethered to the EHR: Primary Care Physician Workload Assessment Using EHR Event Log Data and Time-Motion Observations

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