Ignite Creation Date:
2024-05-06 @ 6:44 PM
Last Modification Date:
2024-10-26 @ 2:53 PM
Study NCT ID:
NCT05765903
Status:
WITHDRAWN
Last Update Posted:
2024-02-15
First Post:
2023-03-01
Brief Title:
UM CRMC RecuR Score Pilot
Sponsor:
University of Maryland Baltimore
Organization:
University of Maryland Baltimore
Study Overview
Official Title:
Readmission Risk Score RecuR Score Pilot at The University of Maryland Charles Regional Medical Center UM CRMC
Status:
WITHDRAWN
Status Verified Date:
2024-02
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Significant protocol modifications needed for feasibility This will require a version 2
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
This study will look to implement a plan for enhanced transitional care for patients at high risk of unplanned hospital readmission in hopes of reducing their risk for readmission in the first 30 days post discharge from an inpatient encounter Hospital readmissions are an undesirable occurrence that can increase cost for hospitals and can cause further negative outcomes for patients Identifying factors that increase a patients chances of being readmitted to the hospital as well as developing an intervention to effectively reduce this risk has historically been challenging
Our new method uses a combination of common features such as diagnosis and length of hospital stay with a novel artificial intelligence AI algorithm the RecuR Score model developed by the University of Maryland Medical System that identifies patients at the highest risk of having an unplanned hospital readmission Participants identified as higher risk will then be enrolled into our pilot where they will be randomized to receive either the standard of care treatment or an enhanced protocol that includes additional disease education coordination of home health services and a focus on their readmission during existing multidisciplinary team huddles
The main goal of this study is to reduce unplanned hospital readmission within 30 days of initial discharge in those most at risk of being readmitted using the aforementioned novel methods for identifying these participants and a transitional care intervention This success of this goal will be analyzed across different readmission risk levels in the study population Secondary goals of this study include reducing unplanned hospital readmission within 90 days reducing 30-day post-discharge mortality and reducing 30- and 90-day emergency department ED usage after an initial hospitalization
Our new method uses a combination of common features such as diagnosis and length of hospital stay with a novel artificial intelligence AI algorithm the RecuR Score model developed by the University of Maryland Medical System that identifies patients at the highest risk of having an unplanned hospital readmission Participants identified as higher risk will then be enrolled into our pilot where they will be randomized to receive either the standard of care treatment or an enhanced protocol that includes additional disease education coordination of home health services and a focus on their readmission during existing multidisciplinary team huddles
The main goal of this study is to reduce unplanned hospital readmission within 30 days of initial discharge in those most at risk of being readmitted using the aforementioned novel methods for identifying these participants and a transitional care intervention This success of this goal will be analyzed across different readmission risk levels in the study population Secondary goals of this study include reducing unplanned hospital readmission within 90 days reducing 30-day post-discharge mortality and reducing 30- and 90-day emergency department ED usage after an initial hospitalization
Detailed Description:
1 BACKGROUND Hospital readmission is an adverse health outcome that incurs significant cost to the healthcare ecosystem While undesired unplanned hospital readmission within 30 days of discharge is not uncommon To improve care quality and reduce unnecessary healthcare costs in 2013 the Center for Medicare and Medicaid Services CMS launched the Hospital Readmissions Reduction Program HRRP as part of the Value Based Purchasing VBP program to encourage better discharge care coordination Under the HRRP program hospitals with high readmission rate incur a payment reduction of up to 3 percent Since the launch of the HRRP program reducing hospital readmissions has elevated to a strategic priority of hospitals Best practices to effectively reduce hospital readmissions while maintaining a healthy operating margin are sought after by hospitals across the country Studies on most optimal intervention structure and intensity have yet to identify a single effective strategy and the effectiveness and external validity of interventions in the literature remains uncertain
Across all patients at University of Maryland Charles Regional Medical Center UM CRMC between January 2019 and January 2022 the unplanned hospital readmission rate was 11 and this value is as high as 30 across certain highest risk groups UM CRMC has implemented a Transitional Care Program with Nurse Navigators since 2011 that focuses on patients that are typically known to have a higher rate of readmission patients whose primary reason for admission is Diabetes Congestive Heart Failure CHF Chronic Obstructive Pulmonary Disease COPD and Hypertension Despite genuine efforts to manage these patients and provide additional support to these patients prior to discharge and post-discharge the readmission rate at UM CRMC has remained relatively unchanged over the past five years between 7-15 mean 11 15 with no sustained year-over-year improvement
At the University of Maryland Medical System UMMS we have developed an artificial intelligence AI-powered risk score called the RecuR Score Readmission Risk Score The RecuR Score estimates the risk of 30-day unplanned readmission for patients both in-house and during the 30 days after inpatient discharge Patients are grouped in one of five score levels 1-5 where a RecuR Score of 1 indicates the lowest risk of readmission and a RecuR Score of 5 indicates the highest risk of readmission This risk score is retrained monthly using data from patients with encounters at UMMS hospitals The target population is inpatients currently in-house non-inpatients Emergency Department Observation Unit who might become inpatients and previous inpatients within 30 days of discharge The score uses data from the UMMS electronic health record system EHR CRISP Chesapeake Regional Information System for our Patients - the state-designated Health Information Exchange for Maryland commercial and non-commercial claims and the US Census Bureau A comparison of the performance of the RecuR Score compared to LACE and HOSPITAL on the same patients showed that the Area Under the Receiver Operating Characteristic Curve sometimes known as the Area Under the Curve AUC of the RecuR Score significantly outperforms the other two metrics even prior to discharge While LACE is only available at discharge HOSPITAL is described as most accurate at discharge and even then it is outperformed by the RecuR Score at 48 hours post-arrival when the RecuR Score is not at its best The literature review shows that efforts to reduce readmission rates are not consistently effective and it has been difficult to extract a set of interventions that reliably reduces readmissions Our team theorizes that efforts to reduce readmission rates are not effective because the patients are not adequately stratified into risk categories resulting in interventions not being used on the patients who will benefit the most from the interventions This pilot addresses this issue by identifying patients at higher risk of readmission using the RecuR Score The RecuR Score accurately identifies patients at high risk of readmission with an area under the ROC curve of 083 This higher risk population limited to selected principal diagnoses and other inclusion and exclusion criteria has a higher readmission rate 194 than the hospitals overall readmission rate 11 which results in a greater opportunity to reduce the readmission rate for the target population
To address the issue of identifying the most effective interventions our team also theorizes that the interventions used are not robust meaning that the impact of the intervention is insufficient For example most readmission intervention programs focus on phone calls post-discharge without considering a more complete view of the patients situation To address this issue this pilot is implementing more complex interventions such as additional educational materials a focus on the patients readmission risk during interdisciplinary medical team huddlescare transition rounds and multiple home healthcare programs that cover a broad spectrum of potential interventions The expectation is that by accurately identifying the higher risk patients and having a broader view of the patients situation with multiple interventions we can reduce the 30-day unplanned readmission rate
2 STUDY OBJECTIVES Offering more complex interventions that are higher intensity than those currently universally provided at UM CRMC By targeting patients who are at a higher risk of readmission using a novel AI-based risk score the RecuR Score resources can be best allocated to those who need them most These more intense interventions include additional educational materials emphasis on a patients readmission risk during their multidisciplinary team huddle and home health services For the study we will only be targeting patients with a high readmission risk based on the patients RecuR Score to test the efficacy of the standardized use of these resources
The first primary hypothesis for this study is that using a novel UMMS algorithm RecuR Score to identify patients at a higher risk of unplanned hospital readmission combined with enhanced pre-discharge and follow-up care interventions including additional educational material about their health a focus on their readmission risk during interdisciplinary team huddles and home health care can reduce 30-day unplanned hospital readmissions in this high-risk group by 30 The second primary hypothesis in this fallback design trial is that using the aforementioned enhanced interventions there will be a 30 reduction of unplanned hospital readmission risk in patients determined to be a medium-high risk of readmission RecuR Score level 2 or 3
3 METHODS This is a parallel-group two-arm prospective randomized non-blinded fallback design controlled superiority study of the impact of a new transitional care model for patients determined to be at higher risk of 30-day unplanned hospital readmission conducted at UM CRMC UM CRMC is an approximately 100-bed community hospital located in Charles County Maryland and is part of the University of Maryland Medical System Patients will be 11 equally randomized to Arm 1 or Arm 2 using a stratified randomization method with stratification by RecuR Score
Participants assigned to Arm 1 will receive the activities in Intervention A and the participants assigned to Arm 2 will receive the activities in Intervention A AND the activities in Intervention B As there is an apparent difference between Arm 1 and Arm 2 neither the patients nor providers will be blinded to the study assignment Informed consent will be obtained and documented for this study
The target patient population is admitted patients inpatients at UM CRMC with a high readmission risk RecuR Score andor specific admission diagnoses that are amenable to peri- and post-discharge interventions where the goal is to reduce the readmission rate of this high-risk population
This study will use a fallback design with two primary endpoints The first sequential primary endpoint is 30-day post-discharge unplanned hospital readmission in the overall study population The second sequential primary endpoint is 30-day post-discharge unplanned hospital readmission in the medium-high risk population RecuR Score 2 3 The significance level 005 will be divided evenly between the two primary endpoints for a significance level of 0025 for the first primary endpoint and a reserved 0025 significance level for the second primary endpoint
4 DATA COLLECTION The patients Electronic Health Record EHR from Epic will be the main source of information about the patients demographics admission diagnoses length of stay and outcomes data like subsequent hospital encounters in the 90 days post-discharge The CRISP Chesapeake Regional Information System for our Patients Admission Discharge Transfer ADT tool is the source that provides what hospital encounters the enrolled patients have post-discharge if they do not occur at UMMS facilities
5 DATA ANALYSIS As this is a fallback method design we will first test the first primary endpoint against a 0025 significance level If the first test shows statistical significance we will be able to add the unused alpha level from the first test to the reserved alpha level for significance level of 005 to test the second primary endpoint If this test fails to show statistical significance we will proceed and test the second primary endpoint at the reserved 0025 significance level
Patient characteristics will be summarized and compared between Arm 1 and Arm 2 for both the overall and RecuR Score 2 and 3 populations Most results will be compared using a test of difference for two proportions If the data are multi-categoric the Mantel-Haenszel method will be used for comparisons If an event is rare 5 the Fisher exact test will be used Any continuous data will be compared using a t-test or Wilcoxon Rank Sum statistic If data are greatly skewed additional methods might be needed These analyses will be used to present baseline and background characteristics demonstrating that the analysis is balanced and representative of the general population or pointing to areas where primary analyses might need to be adjusted to account for imbalances
All analyses of results for primary and secondary endpoints will be based on the Intent-to-Treat principle where results are computed based on the Arm that the patient was randomized to rather than which interventions they actually received All comparisons will be done for both the overall and medium-high risk study populations assuming the study continues to the second primary endpoint Comparison of hospital readmission will be performed using either z-tests or Fischers Exact test depending on the rarity of the outcome of interest If imbalances are noted in the baseline characteristics adjusted regression models will be tested where indicated Subgroup analysis will be performed including a specific focus on differing readmission rates between the various RecuR Score risk levels
Across all patients at University of Maryland Charles Regional Medical Center UM CRMC between January 2019 and January 2022 the unplanned hospital readmission rate was 11 and this value is as high as 30 across certain highest risk groups UM CRMC has implemented a Transitional Care Program with Nurse Navigators since 2011 that focuses on patients that are typically known to have a higher rate of readmission patients whose primary reason for admission is Diabetes Congestive Heart Failure CHF Chronic Obstructive Pulmonary Disease COPD and Hypertension Despite genuine efforts to manage these patients and provide additional support to these patients prior to discharge and post-discharge the readmission rate at UM CRMC has remained relatively unchanged over the past five years between 7-15 mean 11 15 with no sustained year-over-year improvement
At the University of Maryland Medical System UMMS we have developed an artificial intelligence AI-powered risk score called the RecuR Score Readmission Risk Score The RecuR Score estimates the risk of 30-day unplanned readmission for patients both in-house and during the 30 days after inpatient discharge Patients are grouped in one of five score levels 1-5 where a RecuR Score of 1 indicates the lowest risk of readmission and a RecuR Score of 5 indicates the highest risk of readmission This risk score is retrained monthly using data from patients with encounters at UMMS hospitals The target population is inpatients currently in-house non-inpatients Emergency Department Observation Unit who might become inpatients and previous inpatients within 30 days of discharge The score uses data from the UMMS electronic health record system EHR CRISP Chesapeake Regional Information System for our Patients - the state-designated Health Information Exchange for Maryland commercial and non-commercial claims and the US Census Bureau A comparison of the performance of the RecuR Score compared to LACE and HOSPITAL on the same patients showed that the Area Under the Receiver Operating Characteristic Curve sometimes known as the Area Under the Curve AUC of the RecuR Score significantly outperforms the other two metrics even prior to discharge While LACE is only available at discharge HOSPITAL is described as most accurate at discharge and even then it is outperformed by the RecuR Score at 48 hours post-arrival when the RecuR Score is not at its best The literature review shows that efforts to reduce readmission rates are not consistently effective and it has been difficult to extract a set of interventions that reliably reduces readmissions Our team theorizes that efforts to reduce readmission rates are not effective because the patients are not adequately stratified into risk categories resulting in interventions not being used on the patients who will benefit the most from the interventions This pilot addresses this issue by identifying patients at higher risk of readmission using the RecuR Score The RecuR Score accurately identifies patients at high risk of readmission with an area under the ROC curve of 083 This higher risk population limited to selected principal diagnoses and other inclusion and exclusion criteria has a higher readmission rate 194 than the hospitals overall readmission rate 11 which results in a greater opportunity to reduce the readmission rate for the target population
To address the issue of identifying the most effective interventions our team also theorizes that the interventions used are not robust meaning that the impact of the intervention is insufficient For example most readmission intervention programs focus on phone calls post-discharge without considering a more complete view of the patients situation To address this issue this pilot is implementing more complex interventions such as additional educational materials a focus on the patients readmission risk during interdisciplinary medical team huddlescare transition rounds and multiple home healthcare programs that cover a broad spectrum of potential interventions The expectation is that by accurately identifying the higher risk patients and having a broader view of the patients situation with multiple interventions we can reduce the 30-day unplanned readmission rate
2 STUDY OBJECTIVES Offering more complex interventions that are higher intensity than those currently universally provided at UM CRMC By targeting patients who are at a higher risk of readmission using a novel AI-based risk score the RecuR Score resources can be best allocated to those who need them most These more intense interventions include additional educational materials emphasis on a patients readmission risk during their multidisciplinary team huddle and home health services For the study we will only be targeting patients with a high readmission risk based on the patients RecuR Score to test the efficacy of the standardized use of these resources
The first primary hypothesis for this study is that using a novel UMMS algorithm RecuR Score to identify patients at a higher risk of unplanned hospital readmission combined with enhanced pre-discharge and follow-up care interventions including additional educational material about their health a focus on their readmission risk during interdisciplinary team huddles and home health care can reduce 30-day unplanned hospital readmissions in this high-risk group by 30 The second primary hypothesis in this fallback design trial is that using the aforementioned enhanced interventions there will be a 30 reduction of unplanned hospital readmission risk in patients determined to be a medium-high risk of readmission RecuR Score level 2 or 3
3 METHODS This is a parallel-group two-arm prospective randomized non-blinded fallback design controlled superiority study of the impact of a new transitional care model for patients determined to be at higher risk of 30-day unplanned hospital readmission conducted at UM CRMC UM CRMC is an approximately 100-bed community hospital located in Charles County Maryland and is part of the University of Maryland Medical System Patients will be 11 equally randomized to Arm 1 or Arm 2 using a stratified randomization method with stratification by RecuR Score
Participants assigned to Arm 1 will receive the activities in Intervention A and the participants assigned to Arm 2 will receive the activities in Intervention A AND the activities in Intervention B As there is an apparent difference between Arm 1 and Arm 2 neither the patients nor providers will be blinded to the study assignment Informed consent will be obtained and documented for this study
The target patient population is admitted patients inpatients at UM CRMC with a high readmission risk RecuR Score andor specific admission diagnoses that are amenable to peri- and post-discharge interventions where the goal is to reduce the readmission rate of this high-risk population
This study will use a fallback design with two primary endpoints The first sequential primary endpoint is 30-day post-discharge unplanned hospital readmission in the overall study population The second sequential primary endpoint is 30-day post-discharge unplanned hospital readmission in the medium-high risk population RecuR Score 2 3 The significance level 005 will be divided evenly between the two primary endpoints for a significance level of 0025 for the first primary endpoint and a reserved 0025 significance level for the second primary endpoint
4 DATA COLLECTION The patients Electronic Health Record EHR from Epic will be the main source of information about the patients demographics admission diagnoses length of stay and outcomes data like subsequent hospital encounters in the 90 days post-discharge The CRISP Chesapeake Regional Information System for our Patients Admission Discharge Transfer ADT tool is the source that provides what hospital encounters the enrolled patients have post-discharge if they do not occur at UMMS facilities
5 DATA ANALYSIS As this is a fallback method design we will first test the first primary endpoint against a 0025 significance level If the first test shows statistical significance we will be able to add the unused alpha level from the first test to the reserved alpha level for significance level of 005 to test the second primary endpoint If this test fails to show statistical significance we will proceed and test the second primary endpoint at the reserved 0025 significance level
Patient characteristics will be summarized and compared between Arm 1 and Arm 2 for both the overall and RecuR Score 2 and 3 populations Most results will be compared using a test of difference for two proportions If the data are multi-categoric the Mantel-Haenszel method will be used for comparisons If an event is rare 5 the Fisher exact test will be used Any continuous data will be compared using a t-test or Wilcoxon Rank Sum statistic If data are greatly skewed additional methods might be needed These analyses will be used to present baseline and background characteristics demonstrating that the analysis is balanced and representative of the general population or pointing to areas where primary analyses might need to be adjusted to account for imbalances
All analyses of results for primary and secondary endpoints will be based on the Intent-to-Treat principle where results are computed based on the Arm that the patient was randomized to rather than which interventions they actually received All comparisons will be done for both the overall and medium-high risk study populations assuming the study continues to the second primary endpoint Comparison of hospital readmission will be performed using either z-tests or Fischers Exact test depending on the rarity of the outcome of interest If imbalances are noted in the baseline characteristics adjusted regression models will be tested where indicated Subgroup analysis will be performed including a specific focus on differing readmission rates between the various RecuR Score risk levels
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None