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Record W2758459535 · doi:10.2196/iproc.8457

Engaging Heart Failure Patients with Interactive Voice Response Calls and Multimedia Programs as They Transition from Hospital to Home

2017· article· en· W2758459535 on OpenAlexvenueno aff
Mark Mulert, Elizabeth Wolens, Joann Clough, Geri Lynn Baumblatt, Jason Gottlieb

Bibliographic record

VenueIproceedings · 2017
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsHeart failureTransition (genetics)Interactive voice responseMedicineMedical emergencyMultimediaPsychologyComputer scienceInternal medicineTelecommunications

Abstract

fetched live from OpenAlex

Background: When people with heart failure (HF) are discharged from hospitals, they need to manage their condition. Patients are overwhelmed and feel poorly. To avoid complications and readmissions, it’s essential they quickly engage in new behaviors, such as weighing themselves each day. Patients often do not start or maintain these behaviors. Objective: Researchers sought to measure the impact of a user-centered, interactive voice-response (IVR) phone call and multimedia program series (EmmiTransition®) to educate, and motivate patients to take self-care actions post-discharge. Methods: Researchers analyzed call records and conducted aggregate analysis from patients who interacted with the series between August 2013 and May 2016 at UAB Medicine and other healthcare organizations. The 45-day IVR series explains key concepts and behaviors and asks patients to report information like their daily weight. Short multimedia programs provided additional information. Call records from 4,503 patients who completed the series were analyzed. There were 3,615 people who answered and interacted with the calls. Interactions were analyzed to identify the impact on driving people to report their weight daily post-discharge. The percentage of patients who reported weighing themselves daily increased steadily over the first two weeks. After viewing a multimedia program, patients could take an optional survey from Emmi Solutions. Responses and comments were tabulated. Results: On day one of the IVR calls, 66% of UAB Medicine patients who answered the call reported their weight. On day 14 of the calls, 89% of UAB Medicine patients who answered the call reported their weight, comprising a 36% increase in reporting in two weeks. The behaviors seen over the first two weeks were sustained. For the remaining 30 days of the series, 93% of patients who answered calls continued to report their weight. There were 936 patients who opted to take the post-multimedia program survey. The survey findings were as follows: 66% showed increased confidence to ask questions; 75% were prepared to manage their health condition; 75% were more motivated to take their medications; 89% were more aware of how their lifestyle impacts health; 87% were willing to take new action to manage their health; and 88% indicated that they were motivated to change their lifestyle. Examples of patient comments follow: “Read labels and try to decrease processed foods and transfer to whole fruits and vegetables utilizing herbs for seasoning”; “making sure I have a calendar over the bathroom scales to keep track of weight instead of going my memory”; “was not aware diet soda contained high salt count. I will not drink diet sodas as often maybe a glass once or twice a week”; “I will get a flu and pneumonia shot every year. This is not something I did in the past.” Conclusions: Most patients who engaged in this series started a new behavior, regularly reporting they weighed themselves. Most people continued this behavior throughout the 45-day series. Most patients who viewed a multimedia program and completed the survey expressed plans to take specific actions or behavior changes based on information offered by the program about how to weigh themselves, reduce sodium, or manage their fluids.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.624

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.251
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2017
Admission routes1
Has abstractyes

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