Ignite Creation Date:
2025-12-24 @ 6:35 PM
Ignite Modification Date:
2025-12-29 @ 6:21 AM
Study NCT ID:
NCT04604457
Status:
COMPLETED
Last Update Posted:
2021-06-16
First Post:
2020-10-14
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Impact of Predictive Modeling on Time to Palliative Care in an Outpatient Primary Care Population
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'SCREENING', 'interventionModel': 'CROSSOVER', 'interventionModelDescription': '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.'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 127070}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2020-08-31', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2021-06', 'completionDateStruct': {'date': '2021-05-31', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2021-06-14', 'studyFirstSubmitDate': '2020-10-14', 'studyFirstSubmitQcDate': '2020-10-21', 'lastUpdatePostDateStruct': {'date': '2021-06-16', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2020-10-27', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2021-05-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Timely identification for need of palliative care', 'timeFrame': 'Through study completion, an average of 1 year', 'description': 'Time to electronic record of consult by the palliative care team in the outpatient setting'}], 'secondaryOutcomes': [{'measure': 'Number of palliative care consults', 'timeFrame': 'Through study completion, an average of 1 year', 'description': 'Number of palliative care consults that occurred on intervention and standard of care arms'}, {'measure': 'Number of advanced care planning notes documented in the EHR', 'timeFrame': 'Through study completion, an average of 1 year', 'description': 'Number of advanced care planning notes documented in the EHR on both arms'}, {'measure': 'Number of billing codes for palliative care', 'timeFrame': 'Through study completion, an average of 1 year', 'description': 'Number of ICD-10 billing codes for palliative care on both arms'}, {'measure': 'Positive predictive value of screened patients', 'timeFrame': 'Through study completion, an average of 1 year', 'description': 'Percentage of screened patients that received palliative care consults'}, {'measure': 'Percent of patients who are eligible for ECH based palliative care', 'timeFrame': 'Through study completion, an average of 1 year', 'description': 'Percent of patients who are eligible for employee/community health (ECH) based palliative care compared to the Palliative Care Clinic.'}, {'measure': 'Percent agreement between Palliative Care and Primary Care and average time between Primary Care Contact and Response', 'timeFrame': 'Through study completion, an average of 1 year', 'description': '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.'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['machine learning', 'predictive model', 'palliative care'], 'conditions': ['Palliative Care']}, 'referencesModule': {'references': [{'pmid': '36737744', 'type': 'DERIVED', 'citation': '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.'}], 'seeAlsoLinks': [{'url': 'https://www.mayo.edu/research/clinical-trials', 'label': 'Mayo Clinic Clinical Trials'}]}, 'descriptionModule': {'briefSummary': 'A machine learning algorithm will be used to accurately identify patients in certain primary care units who may benefit from palliative care consults.', 'detailedDescription': '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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Adult patient assigned to a primary care unit from July 2020 to June 2021.\n* 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.\n\nExclusion Criteria:\n\n* Patients that have been seen by Palliative care will be excluded for 75 days\n* Patients under the age of 18 years.\n* Patients currently enrolled with hospice'}, 'identificationModule': {'nctId': 'NCT04604457', 'briefTitle': 'Impact of Predictive Modeling on Time to Palliative Care in an Outpatient Primary Care Population', 'organization': {'class': 'OTHER', 'fullName': 'Mayo Clinic'}, 'officialTitle': 'Impact of Predictive Modeling on Time to Palliative Care in an Outpatient Primary Care Population', 'orgStudyIdInfo': {'id': '20-005977'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'NO_INTERVENTION', 'label': 'Standard of Care', 'description': 'Palliative care specialists would not reach out to primary care providers. Palliative care needs would be met via existing mechanisms.'}, {'type': 'EXPERIMENTAL', 'label': 'Predictive Model', 'description': "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.", 'interventionNames': ['Other: Palliative care contacts primary care']}], 'interventions': [{'name': 'Palliative care contacts primary care', 'type': 'OTHER', 'description': '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.', 'armGroupLabels': ['Predictive Model']}]}, 'contactsLocationsModule': {'locations': [{'zip': '55905', 'city': 'Rochester', 'state': 'Minnesota', 'country': 'United States', 'facility': 'Mayo Clinic in Rochester', 'geoPoint': {'lat': 44.02163, 'lon': -92.4699}}], 'overallOfficials': [{'name': 'Rachel Havyer, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Mayo Clinic'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Mayo Clinic', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator', 'investigatorFullName': 'Rachel D. Havyer', 'investigatorAffiliation': 'Mayo Clinic'}}}}