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- Ensayo clínico NCT04046458
De-escalating Vital Sign Checks
Using Predictive Analytics to Reduce Vital Sign Checks in Stable Hospitalized Patients
Descripción general del estudio
Estado
Condiciones
Intervención / Tratamiento
Descripción detallada
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.
Tipo de estudio
Inscripción (Actual)
Fase
- No aplica
Contactos y Ubicaciones
Ubicaciones de estudio
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California
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San Francisco, California, Estados Unidos, 94143
- UCSF
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Criterios de participación
Criterio de elegibilidad
Edades elegibles para estudiar
- Niño
- Adulto
- Adulto Mayor
Acepta Voluntarios Saludables
Géneros elegibles para el estudio
Descripción
Inclusion Criteria:
- All physician teams that operate under the UCSF Division of Hospital Medicine
Exclusion Criteria:
- N/A
Plan de estudios
¿Cómo está diseñado el estudio?
Detalles de diseño
- Propósito principal: Prevención
- Asignación: Aleatorizado
- Modelo Intervencionista: Asignación paralela
- Enmascaramiento: Ninguno (etiqueta abierta)
Armas e Intervenciones
Grupo de participantes/brazo |
Intervención / Tratamiento |
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Experimental: 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.
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Comparador de placebos: No Alert
Physician teams will perform their clinical duties in the EHR as usual, with no visible alert.
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No change to EHR function; no alert visible to providers
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¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
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delirium
Periodo de tiempo: average will be measured at study completion (6 months from study start date - Sep 11, 2019)
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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
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average will be measured at study completion (6 months from study start date - Sep 11, 2019)
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Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
---|---|---|
sleep opportunity
Periodo de tiempo: average will be calculated at study completion (6 months from study start date - Sep 11, 2019)
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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.
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average will be calculated at study completion (6 months from study start date - Sep 11, 2019)
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patient satisfaction
Periodo de tiempo: 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)
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average score will be measured at study completion (6 months from study start date - Sep 11, 2019)
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Otras medidas de resultado
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
---|---|---|
number of code blue events
Periodo de tiempo: 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
Periodo de tiempo: 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 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)
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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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Colaboradores e Investigadores
Patrocinador
Investigadores
- Director de estudio: Mark Pletcher, MD, Director of the UCSF Informatics and Research Innovation Program
Publicaciones y enlaces útiles
Fechas de registro del estudio
Fechas importantes del estudio
Inicio del estudio (Actual)
Finalización primaria (Actual)
Finalización del estudio (Actual)
Fechas de registro del estudio
Enviado por primera vez
Primero enviado que cumplió con los criterios de control de calidad
Publicado por primera vez (Actual)
Actualizaciones de registros de estudio
Última actualización publicada (Actual)
Última actualización enviada que cumplió con los criterios de control de calidad
Última verificación
Más información
Términos relacionados con este estudio
Términos MeSH relevantes adicionales
Otros números de identificación del estudio
- nightvitals
Plan de datos de participantes individuales (IPD)
¿Planea compartir datos de participantes individuales (IPD)?
Descripción del plan IPD
Información sobre medicamentos y dispositivos, documentos del estudio
Estudia un producto farmacéutico regulado por la FDA de EE. UU.
Estudia un producto de dispositivo regulado por la FDA de EE. UU.
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Ensayos clínicos sobre Delirio
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Jubilee Mission Medical College and Research InstituteINCRE fellowship from DBT, govt of IndiaTerminadoSíntomas de abstinencia de alcohol | Delirium Tremens (DT)
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Denver Health and Hospital AuthorityTerminadoDelirio por abstinencia de alcohol | Hiperactividad autonómica asociada a la abstinencia de alcohol | Alucinosis por abstinencia de alcohol | Delirium Tremens inducido por abstinencia de alcoholEstados Unidos
Ensayos clínicos sobre Nighttime Vital Sign EHR Alert
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University Hospital, Basel, SwitzerlandClinical Trial Unit, University Hospital Basel, SwitzerlandTerminado