Palliative CareNCT04604457
Impact of Predictive Modeling on Time to Palliative Care in an Outpatient Primary Care Population
A study testing Palliative care contacts primary care in people with Palliative Care, run by Mayo Clinic at 1 site in United States.
Trial summary
A machine learning algorithm will be used to accurately identify patients in certain primary care units who may benefit from palliative care consults. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Palliative Care
- What is being tested?
- Palliative care contacts primary care
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 127,070 participants
- Where
- 1 siteUnited States
- Study dates
- 31 Aug 2020to 31 May 2021
- 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
- Adult patient assigned to a primary care unit from July 2020 to June 2021.
- Weekly the palliative care specialists will select patients by looking at patients in sorted order starting with the highest score and proceeding down the list and evaluating each patient for exclusion criteria.
Who cannot join
- Patients that have been seen by Palliative care will be excluded for 75 days
- Patients under the age of 18 years.
- Patients currently enrolled with hospice
Trial locations and contacts
1 site in 1 country.
United States1 site
Mayo Clinic in Rochester
Rochester, Minnesota, 55905
About this study
A machine learning algorithm will be used to accurately identify patients in certain primary care units who may benefit from palliative care consults.
Read the detailed description
A machine learning algorithm will be used to accurately identify patients in certain primary care units who may benefit from palliative care consults. These patients will be presented weekly to a palliative care specialist in a custom user interface. The palliative care specialist will reach out to primary care teams if she determines that the patient would benefit from palliative care. If the primary care provider agrees, he/she would write a palliative care consult order for the patient. The goal is to reduce the time to palliative care for these patients, who may not have been identified as quickly without the algorithm.
- Conditions
- Palliative Care
- Being tested
- Palliative care contacts primary care
- Keywords
- machine learning, predictive model, palliative care
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Palliative care contacts primary careOther
Palliative care specialist reaches out to primary care to recommend a palliative care consult. If the primary care provider agrees, he/she will write an order for a palliative care consult.
| Study group | Role | What they receive |
|---|---|---|
| Standard of Care | No intervention | Palliative care specialists would not reach out to primary care providers. Palliative care needs would be met via existing mechanisms. |
| Predictive Model | Experimental | Palliative care specialists review recommendations from the predictive model and contact a patient's primary care provider (PCP) when appropriate to recommend a palliative care consult. Receives: Other: Palliative care contacts primary care |
What is being measured
The primary outcome is the main question the study is designed to answer.
Timely identification for need of palliative care
Time to electronic record of consult by the palliative care team in the outpatient setting
Measured: Through study completion, an average of 1 year
Secondary outcomes (6)
Number of palliative care consults
Number of palliative care consults that occurred on intervention and standard of care arms
Measured: Through study completion, an average of 1 year
Number of advanced care planning notes documented in the EHR
Number of advanced care planning notes documented in the EHR on both arms
Measured: Through study completion, an average of 1 year
Number of billing codes for palliative care
Number of ICD-10 billing codes for palliative care on both arms
Measured: Through study completion, an average of 1 year
Positive predictive value of screened patients
Percentage of screened patients that received palliative care consults
Measured: Through study completion, an average of 1 year
Percent of patients who are eligible for ECH based palliative care
Percent of patients who are eligible for employee/community health (ECH) based palliative care compared to the Palliative Care Clinic.
Measured: Through study completion, an average of 1 year
Percent agreement between Palliative Care and Primary Care and average time between Primary Care Contact and Response
Agreement statistics (percent agreement and Kappa statistics) between Palliative Care and Primary Care and descriptive statistics (mean, etc.) on time between primary care contact and response.
Measured: Through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Oct 2020
- Study start31 Aug 2020
- Primary completion31 May 2021
- Study completion31 May 2021
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
- Screening
- Allocation
- Randomised
- Intervention model
- Crossover
- Model description
- Step-wedge design with 7 wedges: the first wedge has all primary care teams in the standard of care arm; every six weeks one or two care teams switch to the intervention arm.
- Masking
- None (open label)
- Enrolment
- 127,070 participants (actual)
Sponsor and investigators
- Lead sponsor
- Mayo Clinic
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Rachel D. Havyer, Mayo Clinic
- Investigators
- Rachel Havyer, MD, Principal investigator, Mayo Clinic
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04604457
- Sponsor's protocol ID
- 20-005977
- Status last verified
- Jun 2021
- First submitted
- 14 Oct 2020
- Last updated
- 16 Jun 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Heinzen EP, Wilson PM, Storlie CB, Demuth GO, Asai SW, Schaeferle GM, Bartley MM, Havyer RD. Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trial. BMC Palliat Care. 2023 Feb 3;22(1):9. doi: 10.1186/s12904-022-01113-0.
PubMed 36737744
Study at a glance
From the registry record, last updated by the sponsor on 16 Jun 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Impact of Predictive Modeling on Time to Palliative Care in an Outpatient Primary Care Population
- ClinicalTrials.gov ID
- NCT04604457
- Acronym / sponsor ID
- 20-005977
- Conditions
- Palliative Care
- Keywords
- machine learning, predictive model, palliative care
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 127,070 participants (actual)
- Lead sponsor
- Mayo ClinicUniversity, hospital or other
- Study start
- 31 Aug 2020 (actual)
- Primary completion
- 31 May 2021 (actual)
- Study completion
- 31 May 2021 (actual)
- First posted
- 27 Oct 2020 (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.
A machine learning algorithm will be used to accurately identify patients in certain primary care units who may benefit from palliative care consults.
Detailed description
A machine learning algorithm will be used to accurately identify patients in certain primary care units who may benefit from palliative care consults. These patients will be presented weekly to a palliative care specialist in a custom user interface. The palliative care specialist will reach out to primary care teams if she determines that the patient would benefit from palliative care. If the primary care provider agrees, he/she would write a palliative care consult order for the patient. The goal is to reduce the time to palliative care for these patients, who may not have been identified as quickly without the algorithm.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- Step-wedge design with 7 wedges: the first wedge has all primary care teams in the standard of care arm; every six weeks one or two care teams switch to the intervention arm.
| Arm | Type | Receives |
|---|---|---|
| Standard of Care | No intervention | –Palliative care specialists would not reach out to primary care providers. Palliative care needs would be met via existing mechanisms. |
| Predictive Model | Experimental | Other: Palliative care contacts primary carePalliative care specialists review recommendations from the predictive model and contact a patient's primary care provider (PCP) when appropriate to recommend a palliative care consult. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Adult patient assigned to a primary care unit from July 2020 to June 2021.
- Weekly the palliative care specialists will select patients by looking at patients in sorted order starting with the highest score and proceeding down the list and evaluating each patient for exclusion criteria.
Key exclusion criteria (3 listed)
- Patients that have been seen by Palliative care will be excluded for 75 days
- Patients under the age of 18 years.
- Patients currently enrolled with hospice
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 |
|---|---|---|
| Palliative care contacts primary carePalliative care specialist reaches out to primary care to recommend a palliative care consult. If the primary care provider agrees, he/she will write an order for a palliative care consult. | Other | Predictive Model |
Primary outcome
Timely identification for need of palliative care
Time frame: Through study completion, an average of 1 year
Time to electronic record of consult by the palliative care team in the outpatient setting
6 secondary outcomes
Number of palliative care consults
Time frame: Through study completion, an average of 1 year
Number of advanced care planning notes documented in the EHR
Time frame: Through study completion, an average of 1 year
Number of billing codes for palliative care
Time frame: Through study completion, an average of 1 year
Positive predictive value of screened patients
Time frame: Through study completion, an average of 1 year
Percent of patients who are eligible for ECH based palliative care
Time frame: Through study completion, an average of 1 year
Percent agreement between Palliative Care and Primary Care and average time between Primary Care Contact and Response
Time frame: Through study completion, an average of 1 year
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 16 Jun 2021.
- Derived from this study: PubMed 36737744Heinzen EP, Wilson PM, Storlie CB, Demuth GO, Asai SW, Schaeferle GM, Bartley MM, Havyer RD. Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trial. BMC Palliat Care. 2023 Feb 3;22(1):9. doi: 10.1186/s12904-022-01113-0.
- 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.
Rachel Havyer, 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 testing Palliative care contacts primary care in people with 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 2 inclusion and 3 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 16 Jun 2021. 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: Through study completion, an average of 1 year). 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 NCT04604457, 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 16 Jun 2021.