Palliative CareNCT03976297
Artificial Intelligence/Machine Learning Modeling on Time to Palliative Care Review in an Inpatient Hospital Population
A study of Palliative Care, run by Mayo Clinic at 1 site in United States.
Trial summary
Investigators are testing whether machine learning prediction models integrated into a health care model will accurately identify participants who may benefit from a comprehensive review by a palliative care specialist, and decrease time to receiving a palliative care consult in an inpatient setting. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Palliative Care
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 2,231 participants
- Where
- 1 siteUnited States
- Study dates
- 19 Aug 2019to 20 Dec 2020
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Admitted to Mayo Clinic St. Mary's Hospital and Methodist Hospital during August 19, 2019 - August 19, 2020.
- Once a day Monday through Friday, the CT operator selects 12 patients from all of the nursing units that are participating in the trial (whether or not they are currently in the intervention group) with palliative scores of at least 7 (out of 100), i.e., those that are high risk and displayed as red in the CT GUI (unless they are already being seen by palliative care.)
- The CT operator chooses the selected patients by looking at the patients in sorted order starting with the highest score and proceeding down the list, evaluating each patient for exclusion criteria.
- Once the CT operator identifies 12 appropriate patients or once they reaches the end of the high-risk patients (score of 7 or higher) they stop.
Who cannot join
- We will exclude all patients who do not provide research authorization to review their medical records for general research studies in accordance with Minnesota Statute 144.335.
- We will exclude patients under the age of 18 years of age.
- We will exclude patients previously seen by Palliative care during the index hospital visit (i.e., green icon within CT user interface regardless of score)
- We will exclude patient who no longer have an active encounter (patients who have died or patients who have transferred to another facility are excluded) at the time of the review
- We will exclude patients currently enrolled with the Hospice service at Mayo
- We will exclude patients currently enrolled in the Palliative Homebound program (an alternative healthcare model at Mayo)
- We will exclude patients who are about to be discharged in the next 24 hours through indication of note
Trial locations and contacts
1 site in 1 country.
About this study
Official title: The Impact of Artificial Intelligence/Machine Learning (AI/ML) on Time to Palliative Care Review in an Inpatient Hospital Population
Investigators are testing whether machine learning prediction models integrated into a health care model will accurately identify participants who may benefit from a comprehensive review by a palliative care specialist, and decrease time to receiving a palliative care consult in an inpatient setting.
Read the detailed description
The need for timely palliative care is crucial. Aging patient populations are becoming more complex, often needing care from multiple specialties. There has been a growing mismatch between clinical care and patient preferences particularly with regards to services near end-of-life. Research has shown that that most people prefer to die at home despite the majority dying outside of the home (nursing home or hospital). Given the current model of care and incentives palliative care is considered the care of last resort after all attempts at cure have been exhausted. This delay can lead to sub-optimal symptom management for pain and lower quality of life. As the demand for palliative care increases, policy initiatives and referral triage tools to that lead to quality palliative care services are needed.
In 2018 the Mayo Clinic developed a fully integrated information technology (IT) solution focusing on the identification of patients who may benefit from early palliative care review. The tool, known as Control Tower, pulls disparate data sources centered on a machine learning algorithm which predicts the need for palliative care in hospital. This algorithm was put into production as of December 2018 into a silent mode. The algorithm along with other key patient indicators are integrated into a graphical user interface (GUI) which allows a human operator to review the algorithm predictions and subsequently record the operator's assessment. The tool is expected to enhance risk assessment and create a healthcare model in which palliative care can pro-actively and effectively screen for patient need. Anticipated benefits of the approach include improved symptom control and patient satisfaction as well as a measurable impact on inpatient hospital mortality.
The overall objective of this study is to assess the effectiveness and implementation of the Control Tower palliative care algorithm into hospital practice by creating a stepped wedge cluster randomized trial in 16 inpatient units. By creating an algorithm that automatically screens and monitors patient health status during inpatient hospitalization, the investigators hypothesize that participants will receive needed palliative care earlier than under the usual course of care. In addition to testing clinical effectiveness study members will also collect data for process measures to assess the algorithm and healthcare performance after translation of the prediction algorithm from a research domain to a practice setting.
- Conditions
- Palliative Care
- Being tested
- Control Tower
- Keywords
- Machine learning, Artificial Intelligence, Pragmatic Clinical Trial, Healthcare Delivery
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Control TowerOther
A workstation and software tool that extracts medical data from Mayo's data mart and electronic health record, and processes it through a prediction model that determines whether a patient is suited for a palliative care consult.
| Study group | Role | What they receive |
|---|---|---|
| Control Tower Intervention | Experimental | For participants in the intervention arm, the results of the prediction model will be presented through a GUI interface hereby known as the Control Tower. Participants receive scores from Control Tower (0-100; higher score indicating increased need) for palliative care and are subsequently ranked from highest to lowest. Red (7 or greater) is considered high risk. The intervention will include a Control Tower operator who will interact with the inpatient palliative care consult service. The operator will monitor the Control Tower during weekday normal business hours and select daily a cohort of participants in the intervention units with the highest need of palliative care review. The final list of participants will then be sent to palliative care. The palliative care team who is on service will also assess the need for each participants, and those participants which they agree could benefit they will approach the attending clinical team to suggest a palliative care referral. Receives: Other: Control Tower |
| Standard of Care | No intervention | For participants who are not in an intervention period they will receive the standard of care commensurate with their clinical unit. This is feasible given that this is a pragmatic clinical trial where the investigators can easily control the communication between the control tower operator and palliative care team to prevent any contamination between clusters. In addition to the usual source of care control the investigators intentionally have calibrated the prediction model and the Control Tower review to match the average capacity of the palliative care service, knowing that that the team will still receive palliative care consults through the traditional pathway i.e. the attending care team consulting palliative care directly. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Timely identification for need of palliative care
Measured as time in hours to the electronic record of consult by the palliative care team in the inpatient setting.
Measured: 12 months
Secondary outcomes (10)
The number of inpatient palliative care consults
Measured by the rate of palliative care consults in the inpatient units of interest
Measured: 12 months
Timely identification for need of palliative care per unit
Measured as time in hours to the electronic record of consult by the palliative care team in the inpatient setting for each of the 16 nursing units.
Measured: 12 months
Transition time to hospice-designated bed
For all patients with Medicare insurance the time until transferred to a hospice-designated bed from admission.
Measured: 12 months
Time to hospice designation
Measured as time in hours to the electronic record of consult by the hospice care team in the inpatient setting.
Measured: 12 months
Emergency Department visit within 30 days of discharge
Measured by the number of study participants who upon discharge from the inpatient setting are readmitted to the Emergency Department at any Mayo Clinic facility within 30 days.
Measured: 12 months
Hospitalization or readmission within 30 days of discharge
Measured by the number of study participants who upon discharge from the inpatient setting are readmitted to an inpatient unit at any Mayo Clinic facility within 30 days (excluding transfers and planned readmits).
Measured: 12 months
ICU transfers
Measured by the number of study participants who transferred to a intensive care unit during their inpatient stay.
Measured: 12 months
Ratio of inpatient hospice death to non-hospice hospital deaths
Measured by the number of deaths of study participants in hospice designated beds by the number of deaths in non-hospice beds.
Measured: 12 months
Rate of discharge to external hospice
Measured by the number of participants whose electronic health record indicates discharge to external hospice.
Measured: 12 months
Inpatient length of stay
Measured by the difference between admission to first unit to discharge from hospital for all study participants.
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Jun 2019
- Study start19 Aug 2019
- Primary completion18 Nov 2020
- Study completion20 Dec 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Palliative CareStudies of the same condition, open and under way.
- More trials run by Mayo ClinicThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Crossover
- Model description
- Pragmatic Stepped Wedge Cluster Randomized Trial
- Masking
- None (open label)
- Enrolment
- 2,231 participants (actual)
Sponsor and investigators
- Lead sponsor
- Mayo Clinic
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Jon Ebbert, Mayo Clinic
- Investigators
- Jon O Ebbert, MD, Principal investigator, Mayo Clinic
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03976297
- Sponsor's protocol ID
- 19-002315
- Status last verified
- Dec 2020
- First submitted
- 3 Jun 2019
- Last updated
- 30 Dec 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Wilson PM, Philpot LM, Ramar P, Storlie CB, Strand J, Morgan AA, Asai SW, Ebbert JO, Herasevich VD, Soleimani J, Pickering BW. Improving time to palliative care review with predictive modeling in an inpatient adult population: study protocol for a stepped-wedge, pragmatic randomized controlled trial. Trials. 2021 Sep 16;22(1):635. doi: 10.1186/s13063-021-05546-5.
PubMed 34530871
Study at a glance
From the registry record, last updated by the sponsor on 30 Dec 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Impact of Artificial Intelligence/Machine Learning (AI/ML) on Time to Palliative Care Review in an Inpatient Hospital Population
- ClinicalTrials.gov ID
- NCT03976297
- Acronym / sponsor ID
- 19-002315
- Conditions
- Palliative Care
- Keywords
- Machine learning, Artificial Intelligence, Pragmatic Clinical Trial, Healthcare Delivery
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 2,231 participants (actual)
- Lead sponsor
- Mayo ClinicUniversity, hospital or other
- Study start
- 19 Aug 2019 (actual)
- Primary completion
- 18 Nov 2020 (actual)
- Study completion
- 20 Dec 2020 (actual)
- First posted
- 6 Jun 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Investigators are testing whether machine learning prediction models integrated into a health care model will accurately identify participants who may benefit from a comprehensive review by a palliative care specialist, and decrease time to receiving a palliative care consult in an inpatient setting.
Detailed description
The need for timely palliative care is crucial. Aging patient populations are becoming more complex, often needing care from multiple specialties. There has been a growing mismatch between clinical care and patient preferences particularly with regards to services near end-of-life. Research has shown that that most people prefer to die at home despite the majority dying outside of the home (nursing home or hospital). Given the current model of care and incentives palliative care is considered the care of last resort after all attempts at cure have been exhausted. This delay can lead to sub-optimal symptom management for pain and lower quality of life. As the demand for palliative care increases, policy initiatives and referral triage tools to that lead to quality palliative care services are needed.
In 2018 the Mayo Clinic developed a fully integrated information technology (IT) solution focusing on the identification of patients who may benefit from early palliative care review. The tool, known as Control Tower, pulls disparate data sources centered on a machine learning algorithm which predicts the need for palliative care in hospital. This algorithm was put into production as of December 2018 into a silent mode. The algorithm along with other key patient indicators are integrated into a graphical user interface (GUI) which allows a human operator to review the algorithm predictions and subsequently record the operator's assessment. The tool is expected to enhance risk assessment and create a healthcare model in which palliative care can pro-actively and effectively screen for patient need. Anticipated benefits of the approach include improved symptom control and patient satisfaction as well as a measurable impact on inpatient hospital mortality.
The overall objective of this study is to assess the effectiveness and implementation of the Control Tower palliative care algorithm into hospital practice by creating a stepped wedge cluster randomized trial in 16 inpatient units. By creating an algorithm that automatically screens and monitors patient health status during inpatient hospitalization, the investigators hypothesize that participants will receive needed palliative care earlier than under the usual course of care. In addition to testing clinical effectiveness study members will also collect data for process measures to assess the algorithm and healthcare performance after translation of the prediction algorithm from a research domain to a practice setting.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- Pragmatic Stepped Wedge Cluster Randomized Trial
| Arm | Type | Receives |
|---|---|---|
| Control Tower Intervention | Experimental | Other: Control TowerFor participants in the intervention arm, the results of the prediction model will be presented through a GUI interface hereby known as the Control Tower. Participants receive scores from Control Tower (0-100; higher score indicating increased need) for palliative care and are subsequently ranked from highest to lowest. Red (7 or greater) is considered high risk. The intervention will include a Control Tower operator who will interact with the inpatient palliative care consult service. The operator will monitor the Control Tower during weekday normal business hours and select daily a cohort of participants in the intervention units with the highest need of palliative care review. The final list of participants will then be sent to palliative care. The palliative care team who is on service will also assess the need for each participants, and those participants which they agree could benefit they will approach the attending clinical team to suggest a palliative care referral. |
| Standard of Care | No intervention | –For participants who are not in an intervention period they will receive the standard of care commensurate with their clinical unit. This is feasible given that this is a pragmatic clinical trial where the investigators can easily control the communication between the control tower operator and palliative care team to prevent any contamination between clusters. In addition to the usual source of care control the investigators intentionally have calibrated the prediction model and the Control Tower review to match the average capacity of the palliative care service, knowing that that the team will still receive palliative care consults through the traditional pathway i.e. the attending care team consulting palliative care directly. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Admitted to Mayo Clinic St. Mary's Hospital and Methodist Hospital during August 19, 2019 - August 19, 2020.
- Once a day Monday through Friday, the CT operator selects 12 patients from all of the nursing units that are participating in the trial (whether or not they are currently in the intervention group) with palliative scores of at least 7 (out of 100), i.e., those that are high risk and displayed as red in the CT GUI (unless they are already being seen by palliative care.)
- The CT operator chooses the selected patients by looking at the patients in sorted order starting with the highest score and proceeding down the list, evaluating each patient for exclusion criteria.
- Once the CT operator identifies 12 appropriate patients or once they reaches the end of the high-risk patients (score of 7 or higher) they stop.
Key exclusion criteria (7 listed)
- We will exclude all patients who do not provide research authorization to review their medical records for general research studies in accordance with Minnesota Statute 144.335.
- We will exclude patients under the age of 18 years of age.
- We will exclude patients previously seen by Palliative care during the index hospital visit (i.e., green icon within CT user interface regardless of score)
- We will exclude patient who no longer have an active encounter (patients who have died or patients who have transferred to another facility are excluded) at the time of the review
- We will exclude patients currently enrolled with the Hospice service at Mayo
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Control TowerA workstation and software tool that extracts medical data from Mayo's data mart and electronic health record, and processes it through a prediction model that determines whether a patient is suited for a palliative care consult. | Other | Control Tower Intervention |
Primary outcome
Timely identification for need of palliative care
Time frame: 12 months
Measured as time in hours to the electronic record of consult by the palliative care team in the inpatient setting.
10 secondary outcomes
The number of inpatient palliative care consults
Time frame: 12 months
Timely identification for need of palliative care per unit
Time frame: 12 months
Transition time to hospice-designated bed
Time frame: 12 months
Time to hospice designation
Time frame: 12 months
Emergency Department visit within 30 days of discharge
Time frame: 12 months
Hospitalization or readmission within 30 days of discharge
Time frame: 12 months
ICU transfers
Time frame: 12 months
Ratio of inpatient hospice death to non-hospice hospital deaths
Time frame: 12 months
Rate of discharge to external hospice
Time frame: 12 months
Inpatient length of stay
Time frame: 12 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Dec 2020.
- Derived from this study: PubMed 34530871Wilson PM, Philpot LM, Ramar P, Storlie CB, Strand J, Morgan AA, Asai SW, Ebbert JO, Herasevich VD, Soleimani J, Pickering BW. Improving time to palliative care review with predictive modeling in an inpatient adult population: study protocol for a stepped-wedge, pragmatic randomized controlled trial. Trials. 2021 Sep 16;22(1):635. doi: 10.1186/s13063-021-05546-5.
- Mayo Clinic Clinical TrialsLink provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jon O Ebbert, MD principal investigator
Mayo Clinic
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study of Palliative Care, run by Mayo Clinic at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 30 Dec 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Timely identification for need of palliative care (time frame: 12 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03976297, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Dec 2020.