Cited by 74Mobile Health and mHealth ApplicationsMedication Adherence and ComplianceElectronic Health Records Systems

Automated alerts and reminders targeting patients: A review of the literature

Open access
Perri-Moore, Seneca, Kapsandoy, Seraphine, Doyon, Katherine, Hill, Brent, Archer, Melissa, Shane-McWhorter, Laura, Bray, Bruce E., Zeng-Treitler, Qing·2016·Patient Education and Counseling

Abstract

Objective Information technology supporting patient self-management has the potential to foster shared accountability for healthcare outcomes by improving patient adherence. There is growing interest in providing alerts and reminders to patients to improve healthcare self-management. This paper describes a literature review of automated alerts and reminders directed to patients, the technology used, and their efficacy.Methods An electronic literature search was conducted in PubMed to identify relevant studies. The search produced 2418 abstracts; 175 articles underwent full-text review, of which 124 were rejected. 51 publications were included in the final analysis and coding.Results The articles are partitioned into alerts and reminders. A summary of the analysis for the 51 included articles is provided.Conclusion Reminders and alerts are advantageous in many ways; they can be used to reach patients outside of regular clinic settings, be personalized, and there is a minimal age barrier in the efficacy of automated reminders sent to patients. As technologies and patients' proficiencies evolve, the use and dissemination of patient reminders and alerts will also change.Practice implications Automated technology may reliably assist patients to adhere to their health regimen, increase attendance rates, supplement discharge instructions, decrease readmission rates, and potentially reduce clinic costs.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.pec.2015.12.010

Cites

57 of 57 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Implementing personalized care strategies-such as tailored feedback, reminders, and incentives-can significantly boost patient engagement.

    “Studies have shown that implementing personalized care strategies—such as tailored feedback, reminders, and incentives—can significantly boost patient engagement.30,33,34”

    From Program cost and return on investment analysis of remote patient monitoring for hypertension management in the cardiology department of a large healthcare system (2026) · cites this paper as [33]

    The Discussion (section 4.1; page not provided) reports that patients felt more engaged with their care when receiving text messages and were more likely to follow advice when messages were personalized. It also reports benefits from reminders. It does not establish that tailored feedback or patient incentives significantly increase engagement; the incentives it mentions are for providers, and its statistically significant results concern attendance and screening. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. [4]Sands DZ Why participatory medicine? Journal of Participatory Medicine 2009

Cited by

Sorted by citations

Authors
Perri-Moore, S., Kapsandoy, S., Doyon, K., Hill, B., Archer, M., Shane-McWhorter, L., Bray, B. E., & Zeng-Treitler, Q.
Affiliations
  • Intermountain Healthcare (US)
  • University of Utah (US)
Venue
Patient Education and Counseling
Publisher
Elsevier BV
Published
2016-06
Volume
99
Issue
6
Pages
953-959
ISSN
0738-3991 (ISSN-L)
OpenAlex
W2209747928
Cited by
74
References
57
Citations by year
2026: 10 · 2025: 18 · 2024: 5 · 2023: 8 · 2022: 7 · 2021: 8 · 2020: 5 · 2019: 7 · 2018: 3 · 2017: 3
FWCI
3.66
Type
journal article
Language
en
Open access
Locations
  • Patient Education and Counseling (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • U.S. National Library of Medicine (R01LM011334)
  • National Center for Complementary and Integrative Health (R01AT006548)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords
automated alerts, patient reminders, patient self-management, medication adherence, health information technology, shared accountability, personalized reminders, patient attendance, readmission prevention, discharge instructions
MeSH terms
Humans, Patient Compliance, Self Care, Health Behavior, Randomized Controlled Trials as Topic, Reminder Systems, Patient-Centered Care, Cell Phone, Consumer Health Information, Health Communication, Text Messaging

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review