- ICH GCP
- 미국 임상 시험 레지스트리
- 임상시험 NCT04046458
De-escalating Vital Sign Checks
Using Predictive Analytics to Reduce Vital Sign Checks in Stable Hospitalized Patients
연구 개요
상세 설명
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.
연구 유형
등록 (실제)
단계
- 해당 없음
연락처 및 위치
연구 장소
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California
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San Francisco, California, 미국, 94143
- UCSF
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참여기준
자격 기준
공부할 수 있는 나이
- 어린이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
연구 대상 성별
설명
Inclusion Criteria:
- All physician teams that operate under the UCSF Division of Hospital Medicine
Exclusion Criteria:
- N/A
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
- 주 목적: 방지
- 할당: 무작위
- 중재 모델: 병렬 할당
- 마스킹: 없음(오픈 라벨)
무기와 개입
참가자 그룹 / 팔 |
개입 / 치료 |
---|---|
실험적: 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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위약 비교기: 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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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
---|---|---|
delirium
기간: 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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2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
---|---|---|
sleep opportunity
기간: 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
기간: 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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기타 결과 측정
결과 측정 |
측정값 설명 |
기간 |
---|---|---|
number of code blue events
기간: 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
기간: 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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공동 작업자 및 조사자
수사관
- 연구 책임자: Mark Pletcher, MD, Director of the UCSF Informatics and Research Innovation Program
간행물 및 유용한 링크
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
기본 완료 (실제)
연구 완료 (실제)
연구 등록 날짜
최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
기타 연구 ID 번호
- nightvitals
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
IPD 계획 설명
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
미국 FDA 규제 기기 제품 연구
이 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .
Nighttime Vital Sign EHR Alert에 대한 임상 시험
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University Hospital, Basel, SwitzerlandClinical Trial Unit, University Hospital Basel, Switzerland완전한
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Darma Inc.Virginia Commonwealth University완전한