- ICH GCP
- US Clinical Trials Registry
- Klinisk forsøg NCT04046458
De-escalating Vital Sign Checks
Using Predictive Analytics to Reduce Vital Sign Checks in Stable Hospitalized Patients
Studieoversigt
Status
Betingelser
Intervention / Behandling
Detaljeret beskrivelse
Patients in the hospital often report poor sleep. A lack of sleep not only affects a patient's recovery from illness and their overall feeling of wellness, but it is a leading factor in the development of delirium in the hospital. One method for improving sleep in the hospital is to reduce the number of patient care related interruptions that a patient experiences. Vital sign checks at night are one example. In hospitalized patients who are clinically stable, vital sign checks that interrupt sleep are often unnecessary. However, identifying which patients can forego these checks is not a simple task. Currently, the hospital's quality improvement team asks physicians to think about this issue every day and order reduced, or "sleep promotion", vital sign checks on patients they believe could safely tolerate it. The investigators goal is to use a predictive analytics tool to reduce the cognitive burden of this task for busy physicians.
The investigators plan to develop a logistic regression model, trained on data from the electronic health record (EHR), to predict, for a given patient on a given night, whether they could safely tolerate the reduction of overnight vital sign checks. The model will use variables, such as the patient's age, the number of days they have been in the hospital, the vital signs from that day, the lab values from that day, and other clinical variables to make its prediction. The outcome is a binary variable, whether the patient will or will not have abnormal vital signs that night. The training data is retrospective therefore it contains the nighttime vitals that were observed, which the investigators will code as a binary variable and use as the outcome variable for the model to train against.
The investigators will incorporate this algorithm into an EHR alert so physicians can observe its output during their work, and use this information, complemented by their own clinical judgment, to decide about ordering reduced vital sign checks for a given patient.
The investigators will study the effect of this EHR alert on several outcomes: in-hospital delirium (measured by nurse assessment), sleep opportunity (a measurement, based on observational EHR data, of patient care related sleep interruptions), and patient satisfaction (measured by nationally-administered post-hospitalization HCAHPS surveys). Balancing measures, to ensure that reduced vital sign checks do not cause patient harm, will be rapid response calls and code blue calls.
Physician teams will be randomized to either see the EHR alert (intervention arm) or not see the EHR alert.
Undersøgelsestype
Tilmelding (Faktiske)
Fase
- Ikke anvendelig
Kontakter og lokationer
Studiesteder
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-
California
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San Francisco, California, Forenede Stater, 94143
- UCSF
-
-
Deltagelseskriterier
Berettigelseskriterier
Aldre berettiget til at studere
- Barn
- Voksen
- Ældre voksen
Tager imod sunde frivillige
Køn, der er berettiget til at studere
Beskrivelse
Inclusion Criteria:
- All physician teams that operate under the UCSF Division of Hospital Medicine
Exclusion Criteria:
- N/A
Studieplan
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
- Primært formål: Forebyggelse
- Tildeling: Randomiseret
- Interventionel model: Parallel tildeling
- Maskning: Ingen (Åben etiket)
Våben og indgreb
Deltagergruppe / Arm |
Intervention / Behandling |
---|---|
Eksperimentel: EHR Alert
Physician teams will observe the EHR alert as they perform their clinical duties in the EHR.
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A pop-up window in the EHR will notify a physician that their patient has been judged by a predictive algorithm to be safe for reduced overnight vital sign checks.
|
Placebo komparator: No Alert
Physician teams will perform their clinical duties in the EHR as usual, with no visible alert.
|
No change to EHR function; no alert visible to providers
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Hvad måler undersøgelsen?
Primære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
---|---|---|
delirium
Tidsramme: average will be measured at study completion (6 months from study start date - Sep 11, 2019)
|
Nursing Delirium Screening Scale (Nu-DESC score) - assessed by the nurse, can range from zero to ten, a score > 2 has good accuracy for delirium
|
average will be measured at study completion (6 months from study start date - Sep 11, 2019)
|
Sekundære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
---|---|---|
sleep opportunity
Tidsramme: average will be calculated at study completion (6 months from study start date - Sep 11, 2019)
|
a *novel* measurement based on observational EHR data - for every night in the hospital, the investigators can extract from the EHR all event timestamps that could have interrupted the patient's sleep (measured between 11 pm and 6 am).
These are blood pressure recordings, fingerstick glucose checks, blood draws for labs, and not-as-needed medication administrations.
The maximum time period between such events is considered the patient's sleep opportunity for that night (measured in hours).
A higher sleep-opportunity on a given night is better.
The investigators can calculate an average sleep-opportunity for a hospital encounter and then an average sleep-opportunity for all encounters in a clinical trial arm.
|
average will be calculated at study completion (6 months from study start date - Sep 11, 2019)
|
patient satisfaction
Tidsramme: average score will be measured at study completion (6 months from study start date - Sep 11, 2019)
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results from Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) surveys administered to patients after discharge from the hospital (scale is a categorical response: never, sometimes, usually, or always)
|
average score will be measured at study completion (6 months from study start date - Sep 11, 2019)
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Andre resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
---|---|---|
number of code blue events
Tidsramme: average number will be calculated at study completion (6 months from study start date - Sep 11, 2019)
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when a patient has a code blue (respiratory or cardiac arrest) called on them in the hospital, the resuscitation team that responds then writes a note documenting the event; the investigators can count these notes as a proxy for counting code blue events themselves (lower number is better)
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average number will be calculated at study completion (6 months from study start date - Sep 11, 2019)
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number of rapid response calls
Tidsramme: average number will be calculated at study completion (6 months from study start date - Sep 11, 2019)
|
when a patient has a rapid response (significant change in vital signs or alertness) called on them in the hospital, the team that responds writes a note documenting the event and the investigators can count these notes as a proxy for counting rapid response events themselves (lower number is better)
|
average number will be calculated at study completion (6 months from study start date - Sep 11, 2019)
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Samarbejdspartnere og efterforskere
Efterforskere
- Studieleder: Mark Pletcher, MD, Director of the UCSF Informatics and Research Innovation Program
Publikationer og nyttige links
Datoer for undersøgelser
Studer store datoer
Studiestart (Faktiske)
Primær færdiggørelse (Faktiske)
Studieafslutning (Faktiske)
Datoer for studieregistrering
Først indsendt
Først indsendt, der opfyldte QC-kriterier
Først opslået (Faktiske)
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidst verificeret
Mere information
Begreber relateret til denne undersøgelse
Yderligere relevante MeSH-vilkår
Andre undersøgelses-id-numre
- nightvitals
Plan for individuelle deltagerdata (IPD)
Planlægger du at dele individuelle deltagerdata (IPD)?
IPD-planbeskrivelse
Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter
Studerer et amerikansk FDA-reguleret lægemiddelprodukt
Studerer et amerikansk FDA-reguleret enhedsprodukt
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