Literature DB >> 33427675

Differences in Mode Preferences, Response Rates, and Mode Effect Between Automated Email and Phone Survey Systems for Patients of Primary Care Practices: Cross-Sectional Study.

Sharon Johnston1,2, William Hogg2, Sabrina T Wong3, Fred Burge4, Sandra Peterson3.   

Abstract

BACKGROUND: A growing number of health care practices are adopting software systems that link with their existing electronic medical records to generate outgoing phone calls, emails, or text notifications to patients for appointment reminders or practice updates. While practices are adopting this software technology for service notifications to patients, its use for collection of patient-reported measures is still nascent.
OBJECTIVE: This study assessed the mode preferences, response rates, and mode effect for a practice-based automated patient survey using phone and email modalities to patients of primary care practices.
METHODS: This cross-sectional study analyzed responses and respondent demographics for a short, fully automated, telephone or email patient survey sent to individuals within 72 hours of a visit to their regular primary care practice. Each survey consisted of 5 questions drawn from a larger study's patient survey that all respondents completed in the waiting room at the time of their visit. Automated patient survey responses were linked to self-reported sociodemographic information provided on the waiting room survey including age, sex, reported income, and health status.
RESULTS: A total of 871 patients from 87 primary care practices in British Columbia, Ontario, and Nova Scotia, Canada, agreed to the automated patient survey and 470 patients (45.2%) completed all 5 questions on the automated survey. Email administration of the follow-up survey was preferred over phone-based administration, except among patients aged 75 years and older (P<.001). Overall, response rates for those who selected an emailed survey (369/606, 60.9%) were higher (P<.001) than those who selected the phone survey (101/265, 38.1%). This held true irrespective of age, sex, or chronic disease status of individuals. Response rates were also higher for email (range 57.4% [58/101] to 66.3% [108/163]) compared with phone surveys (range 36% [23/64] to 43% [10/23]) for all income groups except the lowest income quintile, which had similar response rates (email: 29/63, 46%; phone: 23/50, 46%) for phone and email modes. We observed moderate (range 64.6% [62/96] to 78.8% [282/358]) agreement between waiting room survey responses and those obtained in the follow-up automated survey. However, overall agreement in responses was poor (range 45.3% [43/95] to 46.2% [43/93]) for 2 questions relating to care coordination.
CONCLUSIONS: An automated practice-based patient experience survey achieved significantly different response rates between phone and email and increased response rates for email as income group rose. Potential mode effects for the different survey modalities may limit multimodal survey approaches. An automated minimal burden patient survey could facilitate the integration of patient-reported outcomes into care planning and service organization, supporting the move of our primary care practices toward a more responsive, patient-centered, continual learning system. However, practices must be attentive to furthering inequities in health care by underrepresenting the experience of certain groups in decision making based on the reach of different survey modes. ©Sharon Johnston, William Hogg, Sabrina T Wong, Fred Burge, Sandra Peterson. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 11.01.2021.

Entities:  

Keywords:  mixed-mode survey; primary care; response rates

Mesh:

Year:  2021        PMID: 33427675      PMCID: PMC7834947          DOI: 10.2196/21240

Source DB:  PubMed          Journal:  J Med Internet Res        ISSN: 1438-8871            Impact factor:   5.428


  10 in total

1.  Conducting waiting room surveys in practice-based primary care research: a user's guide.

Authors:  William Hogg; Sharon Johnston; Grant Russell; Simone Dahrouge; Elizabeth Gyorfi-Dyke; Elizabeth Kristjanssonn
Journal:  Can Fam Physician       Date:  2010-12       Impact factor: 3.275

Review 2.  A scoping review to explore the suitability of interactive voice response to conduct automated performance measurement of the patient's experience in primary care.

Authors:  Michael Falconi; Sharon Johnston; William Hogg
Journal:  Prim Health Care Res Dev       Date:  2015-08-05       Impact factor: 1.458

3.  Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

Authors:  Paul A Harris; Robert Taylor; Robert Thielke; Jonathon Payne; Nathaniel Gonzalez; Jose G Conde
Journal:  J Biomed Inform       Date:  2008-09-30       Impact factor: 6.317

4.  The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005-2008.

Authors:  David Cella; William Riley; Arthur Stone; Nan Rothrock; Bryce Reeve; Susan Yount; Dagmar Amtmann; Rita Bode; Daniel Buysse; Seung Choi; Karon Cook; Robert Devellis; Darren DeWalt; James F Fries; Richard Gershon; Elizabeth A Hahn; Jin-Shei Lai; Paul Pilkonis; Dennis Revicki; Matthias Rose; Kevin Weinfurt; Ron Hays
Journal:  J Clin Epidemiol       Date:  2010-08-04       Impact factor: 6.437

5.  Measuring the patient experience in primary care: Comparing e-mail and waiting room survey delivery in a family health team.

Authors:  Morgan Slater; Tara Kiran
Journal:  Can Fam Physician       Date:  2016-12       Impact factor: 3.275

6.  Assessing methods for measurement of clinical outcomes and quality of care in primary care practices.

Authors:  Michael E Green; William Hogg; Colleen Savage; Sharon Johnston; Grant Russell; R Liisa Jaakkimainen; Richard H Glazier; Janet Barnsley; Richard Birtwhistle
Journal:  BMC Health Serv Res       Date:  2012-07-23       Impact factor: 2.655

7.  The routine collection of patient-reported outcome measures (PROMs) for long-term conditions in primary care: a cohort survey.

Authors:  Michele Peters; Helen Crocker; Crispin Jenkinson; Helen Doll; Ray Fitzpatrick
Journal:  BMJ Open       Date:  2014-02-21       Impact factor: 2.692

8.  Evaluating Digital Maturity and Patient Acceptability of Real-Time Patient Experience Feedback Systems: Systematic Review.

Authors:  Mustafa Khanbhai; Kelsey Flott; Ara Darzi; Erik Mayer
Journal:  J Med Internet Res       Date:  2019-01-14       Impact factor: 5.428

Review 9.  Appointment reminder systems are effective but not optimal: results of a systematic review and evidence synthesis employing realist principles.

Authors:  Sionnadh Mairi McLean; Andrew Booth; Melanie Gee; Sarah Salway; Mark Cobb; Sadiq Bhanbhro; Susan A Nancarrow
Journal:  Patient Prefer Adherence       Date:  2016-04-04       Impact factor: 2.711

10.  Assessing the Use of Mobile Health Technology by Patients: An Observational Study in Primary Care Clinics.

Authors:  Veronica Ramirez; Emily Johnson; Cesar Gonzalez; Vanessa Ramirez; Barbara Rubino; Gina Rossetti
Journal:  JMIR Mhealth Uhealth       Date:  2016-04-19       Impact factor: 4.773

  10 in total
  1 in total

1. 

Authors:  William Hogg; David Bynoe; Doug Archibald; Sharon Johnston
Journal:  Can Fam Physician       Date:  2022-06       Impact factor: 3.025

  1 in total

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