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Integrated Care (IC) Models for Patient-Centered Outcomes

Leveraging Integrated Models of Care to Improve Patient-Centered Outcomes for Publicly-Insured Adults With Complex Health Care Needs

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT03451630
Acronym
IC
Enrollment
1400
Registered
2018-03-02
Start date
2018-09-04
Completion date
2022-11-30
Last updated
2024-12-09

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Anxiety, Asthma, Atrial Fibrillation, Bipolar Disorder, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, Depression, Diabetes, Hypertension, Schizophrenia

Keywords

chronic conditions, care management, digital tools

Brief summary

Multiple chronic conditions (MCC) are widely recognized as the U.S. public health challenge of the 21st century. These physical and behavioral health conditions take a large toll on those living with chronic diseases, including many who are publicly insured, as well as caregivers and society. While evidence-based integrated care models can improve outcomes for individuals with MCC, such models have not yet been widely implemented. Insurance providers/payers have innovative system features that can be used to deploy these models; however, the investigators do not yet know which of these features can best help to improve outcomes for individuals with MCC in general or high-need subgroups in particular. As a result, patients lack information to make important decisions about their health and health care, and system-level decision makers face ongoing challenges in effectively and efficiently supporting those with MCC. This real-world study will provide useful information about available options for supporting individuals with MCC. Building on existing integrated care efforts, the investigators will enroll N=1,400 (a modified total N) adults with MCC at risk for repeated hospitalizations and assess the impact of three payer-led options (e.g. High-Touch, High-Tech, Standard Care/Optimal Discharge Planning (ODP)) on patient-centered outcomes, namely patient activation in health care, health status, and subsequent re-hospitalization. The investigators will also determine which option works best for whom under what circumstances by gathering information directly from individuals with MCC through self-report questionnaires, health care use data, and interviews.

Detailed description

Study aims. Given the documented need for valuable information about system-level features that can be used to effectively and efficiently support adults in living well with MCC, this study is designed to achieve the following aims: Aim 1: Compare the effectiveness of High-Touch, High-Tech, and ODP on primary outcomes including hospital readmission, health status, and patient activation, and on several secondary outcomes including functional status, quality of life, care satisfaction, emergent care use, engagement in primary, specialty, and mental health care, and gaps in care. Aim 2: Examine the differential effects of the interventions for patient subgroups, based on age, race, illness complexity, and comorbid behavioral health conditions to evaluate heterogeneity of treatment effects (HTE) and determine for whom and in what circumstances the interventions are most effective. Aim 3: Examine perceived barriers and facilitators to efficient and effective implementation of High-Touch and High-Tech interventions for delivering evidence-based integrated care. An individual-level randomized design along with a pragmatic, mixed-methods approach to compare system-level features for delivering evidence-based components of integrated care for Medicaid or dual-eligible adult members with MCC who reside in in Western, Central, or Eastern PA and are at high risk for rehospitalization has been selected for this study. This design, based on significant input from patient stakeholders and Drs. Kevin Kraemer (Scientific Co-I; health services researcher) and Doug Landsittel (Co-I; biostatistician/CER expert), accords fully with the PCORI Methodology Standards. Intervention effectiveness will be determined by examining the differential impact on outcomes that are most meaningful to patients in our target population and those delivering their care. The scope and duration of the study interventions and evaluation are sufficient to measure change in patient-centered outcomes. High-Touch, High-Tech, and ODP will serve as the comparators for this study. ODP follows standardized procedures for patient engagement including when a patient is either hospitalized or transitioning from the hospital setting into ambulatory care for follow-up and condition management. Due to resources and other limitations, not all patients who are eligible for High-Touch/High-Tech enroll in these programs. Thus, the addition of the ODP arm will allow for a less intensive model to be examined and targeted to appropriate patient populations. For Aims 1 and 2, an individual, stratified randomized trial design was selected to randomly assign each enrollee to one of the three interventions arms, minimizing and balancing for confounding variables. Individual-level randomization was selected as opposed to cluster randomization at a system level (e.g. practice-, hospital-level) because the interventions are delivered by a single payer and are not subject to within-practice contamination. Based on valuable system-level stakeholder feedback, an unequal randomization ratio of 2:2:1 for High-Touch, High-Tech, and ODP, respectively, was utilized. While the less resource intensive ODP may, in fact, improve meaningful outcomes for certain patient subgroups, the health care system has invested heavily in High-Touch and High-Tech as evidence-based solutions for chronic disease care. Additionally, stakeholders have indicated that they would like as many participants as possible to have a fully integrated care experience offered by High-Touch/High-Tech and would like to limit enrollment into ODP. The investigators will use a mixed-methods approach that incorporates both qualitative and quantitative data. The addition of qualitative data collection and analyses in Aim 3 will permit more comprehensive understanding of patient and staff experiences with the interventions and results will aide in dissemination of study findings in a manner that is most consistent with patient and other stakeholder perceptions and experiences. The overall, four-year study timeline includes three phases: Pre-Intervention (months 1-6), Intervention and Data Collection (months 7-40), and Data Analysis and Reporting (months 41-48). Note: a 19-month no cost extension was granted to the study team to complete enrollment and data collection, especially during workflow adaptations related to COVID-19 restrictions. The study population includes Medicaid or dual-eligible (Medicare-Medicaid) adults age 21 years and older with MCC, including at least one physical health condition (e.g., cardiovascular disease, hypertension, COPD, diabetes) and at least one additional physical or behavioral health condition (e.g., depression, serious mental illness, substance abuse disorder) and at least one hospital discharge in the previous 30 days. These individuals will reside in PA and will be insured through physical and/or behavioral health payers within the UPMC Insurance Services Division (ISD). In addition, these individuals will have several comorbidities, will have been prescribed several medications, and/or will be predicted future high health care utilizers. Based on a 75% enrollment rate, we initially expected1,662 individuals to be randomized to either High-Tech (n=667), High-Touch (n=667) or ODP (n=328). However, our funder approved a sample size recalculation for an 90% retention rate for 1,400 consented individuals randomized to either High-Tech (n=448), High-Touch (n=448) or ODP (n=224). The study will use web-based randomization to one of the three interventions for those individuals who consent to participate in the study. Once a member of the Community Team (CT), multidisciplinary community-based team of nurses, licensed social workers, and licensed professional counselors, determines eligibility, CT personnel will enter key identification information, and the system will then generate a Study ID (numeric identification number) along with assignment to an intervention arm. Randomization will be stratified by gender, type of insurance (Medicaid or Medicare-Medicaid), and technology/digital literacy, which will be assessed at time of enrollment and before randomization, to ensure that intervention arms are balanced with respect to these important variables. Within each stratum, random block sizes of 5 and 10 will be used to maximize balance between intervention groups while minimizing the ability to unmask investigators to the next treatment assignment, triggering an automated alert to CT staff regarding which intervention to implement for each participant and documented accordingly in HealthPlaNET, UPMC ISD's integrated health management software program. If a participant is unwilling to be randomized, they will be excluded from the study. Each patient is assigned a care manager (CM) who provides comprehensive services for the duration of intervention implementation. Bilingual staff will be available to support native Spanish speaking participants. CMs are currently employed to develop and implement care plans with patients, coordinate healthcare services, work with the pharmacist to manage patient's medications, make home visits, and deliver telehealth care and remote monitoring. Patients in both High-Touch and High-Tech will experience similar procedures at the start of their participation. A CM engages patients in a face-to-face assessment in-home or telephonically to dialogue about the social determinants affecting continued hospital readmissions and emergency department use. At the completion of the assessment, the study is presented to the member and if agreeable informed consent occurs. Individuals randomized to ODP will be provided with the transitional care services. High-Touch and High-Tech interventions are provided for four to twelve months following hospitalization, based on need, and ODP participants are transitioned to appropriate Health Plan or community resources within 14 to 30 days.

Interventions

BEHAVIORALHigh-Touch

Intensive, in-person and/or telephonic support.

BEHAVIORALHigh-Tech

Remote care management and self-directed digital tools.

BEHAVIORALOptimal Discharge Planning

Transition to other Health Plan disease management programs and/or community resources.

Sponsors

Patient-Centered Outcomes Research Institute
CollaboratorOTHER
University of Pittsburgh
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
FACTORIAL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE

Intervention model description

We chose an individual, stratified randomized trial design to randomly assign each enrollee to one of the three interventions arms, minimizing and balancing confounding variables. We will utilize an unequal randomization ratio of 2:2:1 for High-Touch, High-Tech, and ODP, respectively. While the less resource intensive ODP, in fact, improve meaningful outcomes for certain patient subgroups, our health care system has invested heavily in High-Touch and High-Tech as evidence-based solutions for chronic disease care. We will use a mixed-methods approach that incorporates both qualitative and quantitative data.

Eligibility

Sex/Gender
ALL
Age
21 Years to No maximum
Healthy volunteers
Yes

Inclusion criteria

1. Medicaid or dual-eligible (Medicare-Medicaid) adults, ages 21 years and older with Multiple Chronic Conditions (MCC). 2. Have at least one physical health condition (e.g., cardiovascular disease, hypertension, COPD, diabetes). 3. Have at least one additional physical or behavioral health condition (e.g., depression, serious mental illness, substance abuse disorder). 4. Reside in Western, Central, or Eastern Pennsylvania. 5. Be insured through physical and/or behavioral health payers within the UPMC ISD. 6. Individuals will have several comorbidities, will have been prescribed several medications, and/or will be predicted future high health care utilizers. 7. Must have at least one hospital discharge within 30 days of enrollment. 8. Speak and read English or Spanish at a 4th grade level.

Exclusion criteria

1. Individuals receiving advanced levels of care, including: * Individuals who are pregnant. * Individuals in skilled nursing facilities or receiving hospice or palliative care. * Individuals on hemodialysis for kidney disease. * Individuals whose inpatient admission was related to active cancer treatment. 2. Individuals currently enrolled in an RPM program. 3. Individuals who have participated in High-Touch or High-Tech within the previous 12 months. 4. Individuals who are unable to operate a smart phone due to limitations in literacy, vision, or dexterity.

Design outcomes

Primary

MeasureTime frameDescription
Patient ActivationBaseline, 3-, 6-, and 12-months.Assessed using the Patient Activation Measure (PAM), a 13-item scale that gauges individual knowledge, skills, and confidence essential to managing one's own health. We assess a global score of the PAM measure, with scores ranging from 0 to 100; lower values represent a poor outcome while higher values represent a better outcome.
Change in Health StatusBaseline, 3-, 6-, and 12-months.Assessed using the RAND 36-Item Short Form Survey 1.0 (SF-36). The SF-36 is a set of 36 health status and quality-of-life measures that are patient self-reported and measure functional health and well-being within eight domains, including physical functioning, role limitations due to physical health, role limitations due to emotional problems, energy/fatigue, emotional well-being, social functioning, pain, and general health. Values are recoded per the scoring key relating each item to the appropriate subscale. All items are scored so that a high score defines a more favorable health state. We assess a global scale with a 0 to 100 range with 0 being worst possible health status and 100 being the best possible health status.
90-Day Hospital Readmission Rate1 to 90 days90-Day Readmissions will be measured using an all-cause readmission rate from inpatient claims for physical and behavioral health service use within 90 days following discharge from the qualifying inpatient admission prior to enrollment in the study.

Secondary

MeasureTime frameDescription
Care SatisfactionBaseline, 3-, 6-, and 12-months.Care satisfaction will be assessed using the Patient Assessment of Care for Chronic Conditions (PACIC) Survey. The PACIC Survey consists of 20-items that measures specific actions or qualities of care that patients report they have experienced in the care of their chronic conditions over the past 6 months. Each item is measured on a scale from 1-5 with 5 signifying higher patient satisfaction and 1 being the lowest. Scoring requires obtaining the mean of all 20 items.
Emergent Care UseAssessed at baseline, 6- and 12-Months.Emergent care use will be measured using existing behavioral and physical health claims data to determine the frequency of emergency department visits within 12-months from enrollment.
Engagement in Primary CareAssessed at baseline, 6- and 12-Months.Engagement in primary care will be measured using existing behavioral and physical health claims determining participant frequency of non-acute visits for participants in the 12 months following enrollment. Because clinical standards of care are 1 primary care (PCP) visit every 12 months, PCP visits are assessed as a Y/N variable at 12-Months.
Engagement in Specialty CareAssessed at baseline, 6- and 12-Months.Engagement in specialty care will be measured using existing behavioral and physical health claims data determining participant frequency of specialty provider visits for participants in the 12 months following enrollment. Specialty care is inclusive of any care provided outside of primary care, physical therapy, or occupational therapy.
Inpatient Readmissions Over 12-MonthsAssessed at baseline, 6- and 12-Months.Readmissions over 12 months will be measured using an all-cause readmission rate from inpatient claims for physical and behavioral health service use within one year following discharge from the qualifying inpatient admission prior to enrollment in the study. Inpatient readmissions were lower than hypothesized for the population. As such, we assessed a Y/N variable for inpatient readmissions at 12-Months.
Mental Health Care VisitsAssessed at baseline, 6- and 12-Months.Assessed using existing behavioral health claims data determining frequency of mental health care visits for participants in the 12 months following enrollment. Because of the low frequency, we assess mental health care visits as a Y/N variable.
30-Day Hospital Readmission Rate1 to 30 days30-Day Readmissions will be measured using an all-cause readmission rate in claims for physical and behavioral health service use within 30 days following discharge from the qualifying inpatient admission prior to enrollment in the study.
Gaps in Care: Chronic Obstructive Pulmonary Disease (COPD)Assessed at baseline, 6- and 12-MonthsGaps in care will be assessed using Healthcare Effectiveness Data and Information Set (HEDIS) quality metrics. For COPD, we assess the percentage of COPD exacerbations for members 40 years of age and older who had an acute inpatient discharge or ED encounter and who were dispensed appropriate medications. Two rates reported: 1. Dispensed a systemic corticosteroid within 14 days of the event (PCE-1) 2. Dispensed a bronchodilator within 30 days of the event (PCE-2)
Gaps in Care: Congestive Heart Failure (CHF)Assessed at 30-days from an index admission discharge.For Gaps in care related to CHF, we assess readmission rate within 30 days after discharge from inpatient stay for members with a diagnosis of CHF prior index hospitalization.
Gaps in Care: Cardiovascular Disease (CVD)Assessed at baseline, 6- and 12-MonthsGaps in care will be assessed using Healthcare Effectiveness Data and Information Set (HEDIS) quality metrics. For CVD, we assess the percentage of males 21-75 years of age and females 40-75 years of age during the measurement year, who were identified as having clinical atherosclerotic cardiovascular disease (ASCVD) and met the following criteria. The following rates are reported: 1. Received Statin Therapy. Members who were dispensed at least one high-intensity or moderate-intensity statin medication during the measurement year (SPC-1). 2. Statin Adherence 80%. Members who remained on a high-intensity or moderate-intensity statin medication for at least 80% of the treatment period (SPC-2).
Gaps in Care: DiabetesAssessed at baseline, 6- and 12-MonthsGaps in care will be assessed using Healthcare Effectiveness Data and Information Set (HEDIS) quality metrics. For diabetes, we assess the percentage of members 40-75 years of age during the measurement year with diabetes who do not have clinical atherosclerotic cardiovascular disease (ASCVD) who met the following criteria. Two rates are reported: 1. Received Statin Therapy. Members who were dispensed at least one statin medication of any intensity during the measurement year (SPD-1). 2. Statin Adherence 80%. Members who remained on a statin medication of any intensity for at least 80% of the treatment period (SPD-2).
Gaps in Care: DepressionAssessed at baseline, 6- and 12-MonthsGaps in care will be assessed using Healthcare Effectiveness Data and Information Set (HEDIS) quality metrics. For depression, we assess the percentage of members diagnosed with a new episode of major depression, treated with antidepressant medication, and who remained on an antidepressant medication for: 1. Effective Acute Phase Treatment - 84 days of continuous treatment during 114-day period following the Index Prescription Start Date (AMM-1). 2. Effective Continuation Phase Treatment - 180 days of continuous treatment during 231-day period following the Index Prescription Start Date (AMM-2).
Gaps in Care: AsthmaAssessed at baseline, 6- and 12-MonthsGaps in care will be assessed using Healthcare Effectiveness Data and Information Set (HEDIS) quality metrics. For asthma, we assess the percentage of members 21-64 years of age during the measurement year who were identified as having persistent asthma and were dispensed appropriate medications that they remained on during the treatment period. Two rates are reported: 1. The percentage of members who remained on an asthma controller medication for at least 50% of their treatment period (MMA-1a). 2. The percentage of members who remained on an asthma controller medication for at least 75% of their treatment period (MMA-1b).
Functional StatusBaseline, 3-, 6-, and 12-months.Assessed using the PROMIS Physical Function - Short Form 6b with six self-reported physical function measures to assess current function, including activities of daily living. Each question has five response options (a 5-point Likert scale) ranging from one to five with 5 being the highest level of physical function and 1 being the lowest. Per best practices, the instrument is scored by Health Measures Scoring Service, using item-level calibrations using responses to each item for each participant, producing a T-score. The highest possible T-score score is 59, indicating the highest level of physical function, and the lowest is 21, indicating the lowest level of physical function.
Quality of LifeBaseline, 3-, 6-, and 12-months.Quality of Life will be assessed using the Quality of Life Enjoyment and Satisfaction Questionnaire - Short Form (Q-LES-Q-SF), which is a self-report measure consisting of 16 questions designed to enable investigators to easily obtain sensitive measures of the degree of enjoyment and satisfaction experienced by subjects in various areas of daily functioning during the past week. The scoring of the Q-LES-Q-SF involves summing only the first 14 items to yield a raw total score, ranging from 14 to 70. The raw total score is calculated into a maximum possible score using the following formula: (raw total score - minimum score)/(maximum possible raw score - minimum score). The minimum raw score on the Q-LES-Q-SF is 14, and the maximum score is 70. Thus, the formula for maximum score can also be written as: (raw score - 14)/56.

Countries

United States

Participant flow

Recruitment details

Enrollment occurred between September 4, 2018 and November 4, 2021. Care Managers enrolled eligible individuals during an initial in-home or telephonic visit; a study team member conducted randomization.

Participants by arm

ArmCount
High-Touch
Delivered primarily via face-to-face interactions, with telephonic interactions and information sharing that does not require access to mobile devices or the Internet. In-person support and/or telephonic interactions to occur at least four times over at least a four-month period. High-Touch: Intensive, in-person and/or telephonic support.
559
High-Tech
Delivered via a remote care management platform and digital health tools. Remote care support interactions to occur for at least a four-month period. High-Tech: Remote care management and self-directed digital tools.
545
Optimal Discharge Planning
Delivered via Health Plan support and resources within 14-30 days of an initial home or telephonic visit. Optimal Discharge Planning: Transition to other Health Plan disease management programs and/or community resources.
283
Total1,387

Withdrawals & dropouts

PeriodReasonFG000FG001FG002
Overall StudyDeath151611
Overall StudyLost to Follow-up904
Overall StudyUnable to confirm accurate eligibility criteria after randomization and intervention completion.343
Overall StudyWithdrawal by Subject030

Baseline characteristics

CharacteristicHigh-TechTotalHigh-TouchOptimal Discharge Planning
Age, Continuous53.59 years
STANDARD_DEVIATION 11.59
53.32 years
STANDARD_DEVIATION 11.72
52.87 years
STANDARD_DEVIATION 11.68
53.67 years
STANDARD_DEVIATION 12.08
Area Deprivation Index (ADI)109.45 units on a scale
STANDARD_DEVIATION 5.62
109.56 units on a scale
STANDARD_DEVIATION 5.38
109.64 units on a scale
STANDARD_DEVIATION 5.29
109.60 units on a scale
STANDARD_DEVIATION 5.11
Charlson Comorbidity Index (CCI)5.14 units on a scale
STANDARD_DEVIATION 3.07
5.06 units on a scale
STANDARD_DEVIATION 3.21
4.94 units on a scale
STANDARD_DEVIATION 3.23
5.16 units on a scale
STANDARD_DEVIATION 3.42
Comfort with Technology/ Digital Literacy
Comfortable: Agree
296 Participants768 Participants310 Participants162 Participants
Comfort with Technology/ Digital Literacy
Comfortable: Agree Strongly
138 Participants339 Participants131 Participants70 Participants
Comfort with Technology/ Digital Literacy
Comfortable: Disagree
70 Participants190 Participants85 Participants35 Participants
Comfort with Technology/ Digital Literacy
Comfortable: Disagree Strongly
41 Participants90 Participants33 Participants16 Participants
Engagement at Baseline
No
213 Participants674 Participants215 Participants246 Participants
Engagement at Baseline
Yes
332 Participants713 Participants344 Participants37 Participants
Ethnicity (NIH/OMB)
Hispanic or Latino
15 Participants39 Participants15 Participants9 Participants
Ethnicity (NIH/OMB)
Not Hispanic or Latino
530 Participants1348 Participants544 Participants274 Participants
Ethnicity (NIH/OMB)
Unknown or Not Reported
0 Participants0 Participants0 Participants0 Participants
Line of Business (Medicaid/ Medicaid-Medicare)
Medicaid
438 Participants1103 Participants441 Participants224 Participants
Line of Business (Medicaid/ Medicaid-Medicare)
Medicare-Medicaid
107 Participants284 Participants118 Participants59 Participants
Race (NIH/OMB)
American Indian or Alaska Native
5 Participants15 Participants7 Participants3 Participants
Race (NIH/OMB)
Asian
0 Participants1 Participants1 Participants0 Participants
Race (NIH/OMB)
Black or African American
120 Participants299 Participants122 Participants57 Participants
Race (NIH/OMB)
More than one race
8 Participants27 Participants10 Participants9 Participants
Race (NIH/OMB)
Native Hawaiian or Other Pacific Islander
3 Participants4 Participants1 Participants0 Participants
Race (NIH/OMB)
Unknown or Not Reported
15 Participants29 Participants4 Participants10 Participants
Race (NIH/OMB)
White
394 Participants1012 Participants414 Participants204 Participants
Sex: Female, Male
Female
345 Participants874 Participants341 Participants188 Participants
Sex: Female, Male
Male
200 Participants513 Participants218 Participants95 Participants

Adverse events

Event typeEG000
affected / at risk
EG001
affected / at risk
EG002
affected / at risk
deaths
Total, all-cause mortality
15 / 55917 / 54511 / 283
other
Total, other adverse events
0 / 5590 / 5450 / 283
serious
Total, serious adverse events
0 / 5590 / 5450 / 283

Outcome results

Primary

90-Day Hospital Readmission Rate

90-Day Readmissions will be measured using an all-cause readmission rate from inpatient claims for physical and behavioral health service use within 90 days following discharge from the qualifying inpatient admission prior to enrollment in the study.

Time frame: 1 to 90 days

Population: 8 participants (0.57%) have missing readmission data. Since 31 participants had more than one admission within 90 days, a binary indicator of readmission within 90 days was generated and used as a primary outcome.

ArmMeasureCategoryValue (COUNT_OF_PARTICIPANTS)
High-Touch90-Day Hospital Readmission RateNo readmission within 90 days472 Participants
High-Touch90-Day Hospital Readmission RateAt least one readmission within 90 days85 Participants
High-Tech90-Day Hospital Readmission RateNo readmission within 90 days470 Participants
High-Tech90-Day Hospital Readmission RateAt least one readmission within 90 days71 Participants
Optimal Discharge Planning90-Day Hospital Readmission RateNo readmission within 90 days240 Participants
Optimal Discharge Planning90-Day Hospital Readmission RateAt least one readmission within 90 days41 Participants
Comparison: Overall Test of Marginal Group Differencesp-value: 0.59Regression, Logistic
Comparison: Test for the Treatment Effectp-value: 0.669295% CI: [0.76, 2.11]Regression, Logistic
Comparison: Test for the Treatment Effectp-value: 0.669295% CI: [0.7, 1.93]Regression, Logistic
Comparison: Test for the Treatment Effectp-value: 0.669295% CI: [0.75, 1.59]Regression, Logistic
Primary

Change in Health Status

Assessed using the RAND 36-Item Short Form Survey 1.0 (SF-36). The SF-36 is a set of 36 health status and quality-of-life measures that are patient self-reported and measure functional health and well-being within eight domains, including physical functioning, role limitations due to physical health, role limitations due to emotional problems, energy/fatigue, emotional well-being, social functioning, pain, and general health. Values are recoded per the scoring key relating each item to the appropriate subscale. All items are scored so that a high score defines a more favorable health state. We assess a global scale with a 0 to 100 range with 0 being worst possible health status and 100 being the best possible health status.

Time frame: Baseline, 3-, 6-, and 12-months.

Population: Includes all individuals who completed the measure at least one timepoint.

ArmMeasureGroupValue (MEAN)Dispersion
High-TouchChange in Health StatusBaseline39.61 score on a scaleStandard Deviation 18.39
High-TouchChange in Health Status3-Month42.12 score on a scaleStandard Deviation 20.06
High-TouchChange in Health Status6-Month41.86 score on a scaleStandard Deviation 19.84
High-TouchChange in Health Status12-Month42.68 score on a scaleStandard Deviation 20.5
High-TechChange in Health Status12-Month42.27 score on a scaleStandard Deviation 19.63
High-TechChange in Health StatusBaseline39.50 score on a scaleStandard Deviation 18.65
High-TechChange in Health Status6-Month41.39 score on a scaleStandard Deviation 19.84
High-TechChange in Health Status3-Month42.03 score on a scaleStandard Deviation 19.9
Optimal Discharge PlanningChange in Health Status12-Month40.85 score on a scaleStandard Deviation 19.03
Optimal Discharge PlanningChange in Health Status3-Month41.31 score on a scaleStandard Deviation 19.99
Optimal Discharge PlanningChange in Health Status6-Month41.83 score on a scaleStandard Deviation 20.84
Optimal Discharge PlanningChange in Health StatusBaseline39.05 score on a scaleStandard Deviation 17.8
Comparison: Overall Test of the Group by Time Interactionp-value: 0.8505Mixed Models Analysis
Comparison: Test for significant change over time when the treatment-by-time interaction effect is not significant.p-value: <0.0001Mixed Models Analysis
Comparison: Test for significant change by treatment when the treatment-by-time interaction effect is not significant.p-value: 0.8656Mixed Models Analysis
Comparison: Test for group difference in the change from baseline to 12-Months using the linear contrasts.p-value: 0.47495% CI: [-1.61, 3.46]Mixed Models Analysis
Comparison: Test for group difference in the change from baseline to 12-Months using the linear contrasts.p-value: 0.601795% CI: [-1.81, 3.13]Mixed Models Analysis
Comparison: Test for group difference in the change from baseline to 12-Months using the linear contrasts.p-value: 0.78695% CI: [-1.67, 2.2]Mixed Models Analysis
Primary

Patient Activation

Assessed using the Patient Activation Measure (PAM), a 13-item scale that gauges individual knowledge, skills, and confidence essential to managing one's own health. We assess a global score of the PAM measure, with scores ranging from 0 to 100; lower values represent a poor outcome while higher values represent a better outcome.

Time frame: Baseline, 3-, 6-, and 12-months.

Population: Includes all individuals who completed the measure at least one timepoint.

ArmMeasureGroupValue (MEAN)Dispersion
High-TouchPatient Activation12-Months63.15 score on a scaleStandard Deviation 15.05
High-TouchPatient ActivationBaseline61.87 score on a scaleStandard Deviation 14.36
High-TouchPatient Activation3-Months63.15 score on a scaleStandard Deviation 15.56
High-TouchPatient Activation6-Months62.55 score on a scaleStandard Deviation 16.03
High-TechPatient Activation3-Months63.60 score on a scaleStandard Deviation 16.65
High-TechPatient Activation12-Months64.58 score on a scaleStandard Deviation 15.41
High-TechPatient Activation6-Months62.15 score on a scaleStandard Deviation 15.84
High-TechPatient ActivationBaseline63.73 score on a scaleStandard Deviation 16.1
Optimal Discharge PlanningPatient Activation12-Months62.83 score on a scaleStandard Deviation 15.75
Optimal Discharge PlanningPatient ActivationBaseline63.82 score on a scaleStandard Deviation 16.12
Optimal Discharge PlanningPatient Activation3-Months63.60 score on a scaleStandard Deviation 15.98
Optimal Discharge PlanningPatient Activation6-Months64.76 score on a scaleStandard Deviation 15.98
Comparison: Overall Test of Group by Time Interactionp-value: 0.0211Mixed Models Analysis
Comparison: Test for group difference in the change from baseline to 12-Months using linear contrast.p-value: 0.02895% CI: [0.29, 5.09]Mixed Models Analysis
Comparison: Test for group difference in the change from baseline to 12-Months using linear contrast.p-value: 0.093595% CI: [-0.35, 4.48]Mixed Models Analysis
Comparison: Test for group difference in the change from baseline to12-Months using linear contrast.p-value: 0.553895% CI: [-1.44, 2.68]Mixed Models Analysis
Secondary

30-Day Hospital Readmission Rate

30-Day Readmissions will be measured using an all-cause readmission rate in claims for physical and behavioral health service use within 30 days following discharge from the qualifying inpatient admission prior to enrollment in the study.

Time frame: 1 to 30 days

Population: Since only 2 participants had more than one admission (2 admissions) within 30 days, a binary indicator of readmission within 30 days was generated and used as the outcome.

ArmMeasureCategoryValue (COUNT_OF_PARTICIPANTS)
High-Touch30-Day Hospital Readmission RateNo readmission within 30 days535 Participants
High-Touch30-Day Hospital Readmission RateAt least one readmission within 30 days24 Participants
High-Tech30-Day Hospital Readmission RateNo readmission within 30 days528 Participants
High-Tech30-Day Hospital Readmission RateAt least one readmission within 30 days17 Participants
Optimal Discharge Planning30-Day Hospital Readmission RateNo readmission within 30 days272 Participants
Optimal Discharge Planning30-Day Hospital Readmission RateAt least one readmission within 30 days11 Participants
Comparison: Overall Test of Marginal Group Differencesp-value: 0.58Regression, Logistic
Comparison: Test for the treatment effectp-value: 0.743695% CI: [0.43, 3.02]Regression, Logistic
Comparison: Test for the treatment effect.p-value: 0.743695% CI: [0.31, 2.25]Regression, Logistic
Comparison: Test for the treatment effect.p-value: 0.743695% CI: [0.62, 2.97]Regression, Logistic
Secondary

Care Satisfaction

Care satisfaction will be assessed using the Patient Assessment of Care for Chronic Conditions (PACIC) Survey. The PACIC Survey consists of 20-items that measures specific actions or qualities of care that patients report they have experienced in the care of their chronic conditions over the past 6 months. Each item is measured on a scale from 1-5 with 5 signifying higher patient satisfaction and 1 being the lowest. Scoring requires obtaining the mean of all 20 items.

Time frame: Baseline, 3-, 6-, and 12-months.

Population: Includes all individuals who completed the measure at least one timepoint.

ArmMeasureGroupValue (MEAN)Dispersion
High-TouchCare SatisfactionBaseline2.96 score on a scaleStandard Deviation 0.98
High-TouchCare Satisfaction3-Month3.02 score on a scaleStandard Deviation 1
High-TouchCare Satisfaction6-Month3.03 score on a scaleStandard Deviation 0.98
High-TouchCare Satisfaction12-Month3.05 score on a scaleStandard Deviation 1.01
High-TechCare Satisfaction12-Month3.03 score on a scaleStandard Deviation 1.01
High-TechCare SatisfactionBaseline2.86 score on a scaleStandard Deviation 0.94
High-TechCare Satisfaction6-Month2.97 score on a scaleStandard Deviation 1.01
High-TechCare Satisfaction3-Month2.99 score on a scaleStandard Deviation 0.99
Optimal Discharge PlanningCare Satisfaction12-Month3.00 score on a scaleStandard Deviation 0.99
Optimal Discharge PlanningCare Satisfaction3-Month2.97 score on a scaleStandard Deviation 0.97
Optimal Discharge PlanningCare Satisfaction6-Month2.93 score on a scaleStandard Deviation 1
Optimal Discharge PlanningCare SatisfactionBaseline2.96 score on a scaleStandard Deviation 1
Comparison: Overall test of treatment-by-timep-value: 0.1884Mixed Models Analysis
Comparison: Test for the change over time when the treatment-by-time interaction effect is not significant.p-value: 0.009Mixed Models Analysis
Comparison: Test for the change by treatment when the treatment-by-time interaction effect is not significant.p-value: 0.217Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12 months using the linear contrasts.p-value: 0.19195% CI: [-0.06, 0.28]Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12 months using the linear contrasts.p-value: 0.022995% CI: [0.03, 0.35]Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12 months using the linear contrasts.p-value: 0.254795% CI: [-0.21, 0.06]Mixed Models Analysis
Secondary

Emergent Care Use

Emergent care use will be measured using existing behavioral and physical health claims data to determine the frequency of emergency department visits within 12-months from enrollment.

Time frame: Assessed at baseline, 6- and 12-Months.

Population: For each timepoint, participants who were eligible for Medicaid/Medicaid-Medicare with available claims data during at least 9 months (non-continuous) of the past 12 months.

ArmMeasureGroupValue (MEAN)Dispersion
High-TouchEmergent Care Use6-Month1.33 visitsStandard Deviation 2.08
High-TouchEmergent Care UseBaseline2.83 visitsStandard Deviation 3.25
High-TouchEmergent Care Use12-Month2.41 visitsStandard Deviation 3.58
High-TechEmergent Care Use6-Month1.31 visitsStandard Deviation 2.44
High-TechEmergent Care UseBaseline2.40 visitsStandard Deviation 3.36
High-TechEmergent Care Use12-Month2.40 visitsStandard Deviation 4.32
Optimal Discharge PlanningEmergent Care UseBaseline2.39 visitsStandard Deviation 3.73
Optimal Discharge PlanningEmergent Care Use12-Month2.29 visitsStandard Deviation 3.29
Optimal Discharge PlanningEmergent Care Use6-Month1.25 visitsStandard Deviation 2.02
Comparison: Overall test for the treatment-by-time interactionp-value: 0.0413Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12-Months using the linear contrasts.p-value: 0.143395% CI: [-0.28, 0.04]Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12-Months using the linear contrasts.p-value: 0.427295% CI: [-0.1, 0.23]Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12-Months using the linear contrasts.p-value: 0.003795% CI: [-0.31, -0.06]Mixed Models Analysis
Secondary

Engagement in Primary Care

Engagement in primary care will be measured using existing behavioral and physical health claims determining participant frequency of non-acute visits for participants in the 12 months following enrollment. Because clinical standards of care are 1 primary care (PCP) visit every 12 months, PCP visits are assessed as a Y/N variable at 12-Months.

Time frame: Assessed at baseline, 6- and 12-Months.

Population: For each timepoint, participants who were eligible for Medicaid/Medicaid-Medicare with available claims data during at least 9 months (non-continuous) of the past 12 months.

ArmMeasureGroupCategoryValue (COUNT_OF_PARTICIPANTS)
High-TouchEngagement in Primary Care6-MonthsAt least one PCP visit467 Participants
High-TouchEngagement in Primary Care12-MonthsMissing Data30 Participants
High-TouchEngagement in Primary Care6-MonthsMissing Data8 Participants
High-TouchEngagement in Primary Care6-MonthsNo PCP visit84 Participants
High-TouchEngagement in Primary CareBaselineAt least one PCP visit451 Participants
High-TouchEngagement in Primary Care12-MonthsNo PCP visit62 Participants
High-TouchEngagement in Primary CareBaselineMissing Data44 Participants
High-TouchEngagement in Primary CareBaselineNo PCP visit64 Participants
High-TouchEngagement in Primary Care12-MonthsAt least one PCP visit467 Participants
High-TechEngagement in Primary Care6-MonthsNo PCP visit72 Participants
High-TechEngagement in Primary CareBaselineAt least one PCP visit468 Participants
High-TechEngagement in Primary CareBaselineNo PCP visit41 Participants
High-TechEngagement in Primary CareBaselineMissing Data36 Participants
High-TechEngagement in Primary Care6-MonthsAt least one PCP visit463 Participants
High-TechEngagement in Primary Care6-MonthsMissing Data10 Participants
High-TechEngagement in Primary Care12-MonthsAt least one PCP visit476 Participants
High-TechEngagement in Primary Care12-MonthsNo PCP visit44 Participants
High-TechEngagement in Primary Care12-MonthsMissing Data25 Participants
Optimal Discharge PlanningEngagement in Primary CareBaselineMissing Data29 Participants
Optimal Discharge PlanningEngagement in Primary CareBaselineAt least one PCP visit224 Participants
Optimal Discharge PlanningEngagement in Primary Care12-MonthsAt least one PCP visit238 Participants
Optimal Discharge PlanningEngagement in Primary CareBaselineNo PCP visit30 Participants
Optimal Discharge PlanningEngagement in Primary Care12-MonthsMissing Data23 Participants
Optimal Discharge PlanningEngagement in Primary Care6-MonthsNo PCP visit32 Participants
Optimal Discharge PlanningEngagement in Primary Care6-MonthsAt least one PCP visit242 Participants
Optimal Discharge PlanningEngagement in Primary Care12-MonthsNo PCP visit22 Participants
Optimal Discharge PlanningEngagement in Primary Care6-MonthsMissing Data9 Participants
Comparison: Overall test for the treatment-by-time interactionp-value: 0.8231Mixed Models Analysis
Comparison: Test for the change over time when the treatment-by-time interaction effect is not significant.p-value: <0.0001Mixed Models Analysis
Comparison: Test for the change by treatment when the treatment-by-time interaction effect is not significant.p-value: 0.6061Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12-Months using the linear contrasts.p-value: 0.745995% CI: [-0.15, 0.11]Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12-Months using the linear contrasts.p-value: 0.316995% CI: [-0.18, 0.08]Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12-Months using the linear contrasts.p-value: 0.409895% CI: [-0.07, 0.14]Mixed Models Analysis
Secondary

Engagement in Specialty Care

Engagement in specialty care will be measured using existing behavioral and physical health claims data determining participant frequency of specialty provider visits for participants in the 12 months following enrollment. Specialty care is inclusive of any care provided outside of primary care, physical therapy, or occupational therapy.

Time frame: Assessed at baseline, 6- and 12-Months.

Population: For each timepoint, participants who were eligible for Medicaid/Medicaid-Medicare with available claims data during at least 9 months (non-continuous) of the past 12 months.

ArmMeasureGroupValue (MEAN)Dispersion
High-TouchEngagement in Specialty Care6-Month4.15 visitsStandard Deviation 5.34
High-TouchEngagement in Specialty CareBaseline7.29 visitsStandard Deviation 10.2
High-TouchEngagement in Specialty Care12-Month7.80 visitsStandard Deviation 9.02
High-TechEngagement in Specialty Care6-Month4.51 visitsStandard Deviation 6.53
High-TechEngagement in Specialty CareBaseline7.54 visitsStandard Deviation 8.89
High-TechEngagement in Specialty Care12-Month8.31 visitsStandard Deviation 10.39
Optimal Discharge PlanningEngagement in Specialty CareBaseline7.20 visitsStandard Deviation 8.5
Optimal Discharge PlanningEngagement in Specialty Care12-Month7.84 visitsStandard Deviation 9.33
Optimal Discharge PlanningEngagement in Specialty Care6-Month4.08 visitsStandard Deviation 5.16
Comparison: Overall test for the treatment-by-time interactionp-value: 0.4732Mixed Models Analysis
Comparison: Test for the change over time when the treatment-by-time interaction effect is not significant.p-value: <0.0001Mixed Models Analysis
Comparison: Test for the change by treatment when the treatment-by-time interaction effect is not significant.p-value: 0.9628Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12-Months using the linear contrasts.p-value: 0.843895% CI: [-0.16, 0.19]Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12-Months using the linear contrasts.p-value: 0.742595% CI: [-0.19, 0.15]Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12-Months using the linear contrasts.p-value: 0.504495% CI: [-0.1, 0.17]Mixed Models Analysis
Secondary

Functional Status

Assessed using the PROMIS Physical Function - Short Form 6b with six self-reported physical function measures to assess current function, including activities of daily living. Each question has five response options (a 5-point Likert scale) ranging from one to five with 5 being the highest level of physical function and 1 being the lowest. Per best practices, the instrument is scored by Health Measures Scoring Service, using item-level calibrations using responses to each item for each participant, producing a T-score. The highest possible T-score score is 59, indicating the highest level of physical function, and the lowest is 21, indicating the lowest level of physical function.

Time frame: Baseline, 3-, 6-, and 12-months.

Population: Includes all individuals who completed the measure at least one timepoint.

ArmMeasureGroupValue (MEAN)Dispersion
High-TouchFunctional StatusBaseline36.75 T scoreStandard Deviation 7.89
High-TouchFunctional Status3-Month37.11 T scoreStandard Deviation 7.65
High-TouchFunctional Status6-Month37.30 T scoreStandard Deviation 7.87
High-TouchFunctional Status12-Month36.99 T scoreStandard Deviation 8.65
High-TechFunctional Status12-Month36.73 T scoreStandard Deviation 8.14
High-TechFunctional StatusBaseline36.29 T scoreStandard Deviation 7.98
High-TechFunctional Status6-Month36.85 T scoreStandard Deviation 7.76
High-TechFunctional Status3-Month37.02 T scoreStandard Deviation 7.97
Optimal Discharge PlanningFunctional Status12-Month36.29 T scoreStandard Deviation 7.66
Optimal Discharge PlanningFunctional Status3-Month36.83 T scoreStandard Deviation 8.2
Optimal Discharge PlanningFunctional Status6-Month37.24 T scoreStandard Deviation 7.91
Optimal Discharge PlanningFunctional StatusBaseline35.78 T scoreStandard Deviation 7.6
Comparison: Overall test of treatment-by-time interactionp-value: 0.5558Mixed Models Analysis
Comparison: Test for the change over time when the treatment-by-time interaction effect is not significant.p-value: 0.0002Mixed Models Analysis
Comparison: Test for the change by treatment when the treatment-by-time interaction effect is not significant.p-value: 0.7846Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12 months using the linear contrasts.p-value: 0.244495% CI: [-1.92, 0.49]Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12 months using the linear contrasts.p-value: 0.64895% CI: [-1.48, 0.92]Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12 months using the linear contrasts.p-value: 0.347695% CI: [-1.34, 0.47]Mixed Models Analysis
Secondary

Gaps in Care: Asthma

Gaps in care will be assessed using Healthcare Effectiveness Data and Information Set (HEDIS) quality metrics. For asthma, we assess the percentage of members 21-64 years of age during the measurement year who were identified as having persistent asthma and were dispensed appropriate medications that they remained on during the treatment period. Two rates are reported: 1. The percentage of members who remained on an asthma controller medication for at least 50% of their treatment period (MMA-1a). 2. The percentage of members who remained on an asthma controller medication for at least 75% of their treatment period (MMA-1b).

Time frame: Assessed at baseline, 6- and 12-Months

Population: Only a very small portion of our total sample size was eligible for Gaps in Care analyses.

ArmMeasureGroupCategoryValue (COUNT_OF_PARTICIPANTS)
High-TouchGaps in Care: AsthmaBaseline MMA-1a (50%)Did not close gap in care6 Participants
High-TouchGaps in Care: AsthmaBaseline MMA-1a (50%)Closed gap in care2 Participants
High-TouchGaps in Care: Asthma6-Month MMA-1a (50%)Did not close gap in care2 Participants
High-TouchGaps in Care: Asthma6-Month MMA-1a (50%)Closed gap in care2 Participants
High-TouchGaps in Care: Asthma12-Month MMA-1a (50%)Did not close gap in care1 Participants
High-TouchGaps in Care: Asthma12-Month MMA-1a (50%)Closed gap in care5 Participants
High-TouchGaps in Care: AsthmaBaseline MMA-1b (75%)Did not close gap in care6 Participants
High-TouchGaps in Care: AsthmaBaseline MMA-1b (75%)Closed gap in care2 Participants
High-TouchGaps in Care: Asthma6-Month MMA-1b (75%)Did not close gap in care2 Participants
High-TouchGaps in Care: Asthma6-Month MMA-1b (75%)Closed gap in care2 Participants
High-TouchGaps in Care: Asthma12-Month MMA-1b (75%)Did not close gap in care1 Participants
High-TouchGaps in Care: Asthma12-Month MMA-1b (75%)Closed gap in care5 Participants
High-TechGaps in Care: Asthma12-Month MMA-1b (75%)Closed gap in care3 Participants
High-TechGaps in Care: AsthmaBaseline MMA-1a (50%)Did not close gap in care3 Participants
High-TechGaps in Care: AsthmaBaseline MMA-1b (75%)Did not close gap in care9 Participants
High-TechGaps in Care: Asthma6-Month MMA-1b (75%)Did not close gap in care4 Participants
High-TechGaps in Care: AsthmaBaseline MMA-1a (50%)Closed gap in care10 Participants
High-TechGaps in Care: Asthma12-Month MMA-1a (50%)Closed gap in care6 Participants
High-TechGaps in Care: Asthma12-Month MMA-1b (75%)Did not close gap in care4 Participants
High-TechGaps in Care: Asthma6-Month MMA-1a (50%)Did not close gap in care3 Participants
High-TechGaps in Care: AsthmaBaseline MMA-1b (75%)Closed gap in care4 Participants
High-TechGaps in Care: Asthma12-Month MMA-1a (50%)Did not close gap in care1 Participants
High-TechGaps in Care: Asthma6-Month MMA-1a (50%)Closed gap in care8 Participants
High-TechGaps in Care: Asthma6-Month MMA-1b (75%)Closed gap in care7 Participants
Optimal Discharge PlanningGaps in Care: Asthma6-Month MMA-1a (50%)Closed gap in care2 Participants
Optimal Discharge PlanningGaps in Care: Asthma12-Month MMA-1a (50%)Did not close gap in care0 Participants
Optimal Discharge PlanningGaps in Care: Asthma6-Month MMA-1b (75%)Closed gap in care2 Participants
Optimal Discharge PlanningGaps in Care: Asthma12-Month MMA-1a (50%)Closed gap in care2 Participants
Optimal Discharge PlanningGaps in Care: AsthmaBaseline MMA-1b (75%)Did not close gap in care3 Participants
Optimal Discharge PlanningGaps in Care: AsthmaBaseline MMA-1b (75%)Closed gap in care3 Participants
Optimal Discharge PlanningGaps in Care: Asthma12-Month MMA-1b (75%)Did not close gap in care0 Participants
Optimal Discharge PlanningGaps in Care: AsthmaBaseline MMA-1a (50%)Did not close gap in care3 Participants
Optimal Discharge PlanningGaps in Care: AsthmaBaseline MMA-1a (50%)Closed gap in care3 Participants
Optimal Discharge PlanningGaps in Care: Asthma6-Month MMA-1b (75%)Did not close gap in care1 Participants
Optimal Discharge PlanningGaps in Care: Asthma6-Month MMA-1a (50%)Did not close gap in care1 Participants
Optimal Discharge PlanningGaps in Care: Asthma12-Month MMA-1b (75%)Closed gap in care2 Participants
Comparison: Frequency and test of the marginal association between event rate and treatment for eligible participants' data at 12 months for MMA-1ap-value: 1Fisher Exact
Comparison: Frequency and test of the marginal association between event rate and treatment for eligible participants' data at 12 months for MMA-1bp-value: 0.242Fisher Exact
Secondary

Gaps in Care: Cardiovascular Disease (CVD)

Gaps in care will be assessed using Healthcare Effectiveness Data and Information Set (HEDIS) quality metrics. For CVD, we assess the percentage of males 21-75 years of age and females 40-75 years of age during the measurement year, who were identified as having clinical atherosclerotic cardiovascular disease (ASCVD) and met the following criteria. The following rates are reported: 1. Received Statin Therapy. Members who were dispensed at least one high-intensity or moderate-intensity statin medication during the measurement year (SPC-1). 2. Statin Adherence 80%. Members who remained on a high-intensity or moderate-intensity statin medication for at least 80% of the treatment period (SPC-2).

Time frame: Assessed at baseline, 6- and 12-Months

Population: Only a very small portion of the total sample was eligible for CVD Gaps in Care analyses.

ArmMeasureGroupCategoryValue (COUNT_OF_PARTICIPANTS)
High-TouchGaps in Care: Cardiovascular Disease (CVD)Baseline SPC-1Did not close gap in care7 Participants
High-TouchGaps in Care: Cardiovascular Disease (CVD)Baseline SPC-1Closed gap in care38 Participants
High-TouchGaps in Care: Cardiovascular Disease (CVD)6-Month SPC-1Did not close gap in care8 Participants
High-TouchGaps in Care: Cardiovascular Disease (CVD)6-Month SPC-1Closed gap in care42 Participants
High-TouchGaps in Care: Cardiovascular Disease (CVD)12-Month SPC-1Did not close gap in care11 Participants
High-TouchGaps in Care: Cardiovascular Disease (CVD)12-Month SPC-1Closed gap in care48 Participants
High-TouchGaps in Care: Cardiovascular Disease (CVD)Baseline SPC-2Did not close gap in care16 Participants
High-TouchGaps in Care: Cardiovascular Disease (CVD)Baseline SPC-2Closed gap in care22 Participants
High-TouchGaps in Care: Cardiovascular Disease (CVD)6-Month SPC-2Did not close gap in care11 Participants
High-TouchGaps in Care: Cardiovascular Disease (CVD)6-Month SPC-2Closed gap in care31 Participants
High-TouchGaps in Care: Cardiovascular Disease (CVD)12-Month SPC-2Did not close gap in care14 Participants
High-TouchGaps in Care: Cardiovascular Disease (CVD)12-Month SPC-2Closed gap in care34 Participants
High-TechGaps in Care: Cardiovascular Disease (CVD)12-Month SPC-2Closed gap in care46 Participants
High-TechGaps in Care: Cardiovascular Disease (CVD)Baseline SPC-1Did not close gap in care4 Participants
High-TechGaps in Care: Cardiovascular Disease (CVD)Baseline SPC-2Did not close gap in care6 Participants
High-TechGaps in Care: Cardiovascular Disease (CVD)6-Month SPC-2Did not close gap in care17 Participants
High-TechGaps in Care: Cardiovascular Disease (CVD)Baseline SPC-1Closed gap in care38 Participants
High-TechGaps in Care: Cardiovascular Disease (CVD)12-Month SPC-1Closed gap in care69 Participants
High-TechGaps in Care: Cardiovascular Disease (CVD)12-Month SPC-2Did not close gap in care23 Participants
High-TechGaps in Care: Cardiovascular Disease (CVD)6-Month SPC-1Did not close gap in care5 Participants
High-TechGaps in Care: Cardiovascular Disease (CVD)Baseline SPC-2Closed gap in care32 Participants
High-TechGaps in Care: Cardiovascular Disease (CVD)12-Month SPC-1Did not close gap in care10 Participants
High-TechGaps in Care: Cardiovascular Disease (CVD)6-Month SPC-1Closed gap in care49 Participants
High-TechGaps in Care: Cardiovascular Disease (CVD)6-Month SPC-2Closed gap in care32 Participants
Optimal Discharge PlanningGaps in Care: Cardiovascular Disease (CVD)6-Month SPC-1Closed gap in care26 Participants
Optimal Discharge PlanningGaps in Care: Cardiovascular Disease (CVD)12-Month SPC-1Did not close gap in care7 Participants
Optimal Discharge PlanningGaps in Care: Cardiovascular Disease (CVD)6-Month SPC-2Closed gap in care15 Participants
Optimal Discharge PlanningGaps in Care: Cardiovascular Disease (CVD)12-Month SPC-1Closed gap in care33 Participants
Optimal Discharge PlanningGaps in Care: Cardiovascular Disease (CVD)Baseline SPC-2Did not close gap in care7 Participants
Optimal Discharge PlanningGaps in Care: Cardiovascular Disease (CVD)Baseline SPC-2Closed gap in care15 Participants
Optimal Discharge PlanningGaps in Care: Cardiovascular Disease (CVD)12-Month SPC-2Did not close gap in care14 Participants
Optimal Discharge PlanningGaps in Care: Cardiovascular Disease (CVD)Baseline SPC-1Did not close gap in care5 Participants
Optimal Discharge PlanningGaps in Care: Cardiovascular Disease (CVD)Baseline SPC-1Closed gap in care22 Participants
Optimal Discharge PlanningGaps in Care: Cardiovascular Disease (CVD)6-Month SPC-2Did not close gap in care11 Participants
Optimal Discharge PlanningGaps in Care: Cardiovascular Disease (CVD)6-Month SPC-1Did not close gap in care6 Participants
Optimal Discharge PlanningGaps in Care: Cardiovascular Disease (CVD)12-Month SPC-2Closed gap in care19 Participants
Comparison: Test for the treatment-by-time interaction (without weight) for SPC-1.p-value: 0.9912Mixed Models Analysis
Comparison: Test for change over time for SPC-1p-value: 0.876Mixed Models Analysis
Comparison: Test for change in treatment effect for SPC-1p-value: 0.678Mixed Models Analysis
Comparison: Test for the treatment-by-time interaction (without weight) for SPC-2p-value: 0.1761Mixed Models Analysis
Comparison: Test for change over time for SPC-2p-value: 0.3239Mixed Models Analysis
Comparison: Test for change in treatment effect for SPC-2p-value: 0.7415Mixed Models Analysis
Secondary

Gaps in Care: Chronic Obstructive Pulmonary Disease (COPD)

Gaps in care will be assessed using Healthcare Effectiveness Data and Information Set (HEDIS) quality metrics. For COPD, we assess the percentage of COPD exacerbations for members 40 years of age and older who had an acute inpatient discharge or ED encounter and who were dispensed appropriate medications. Two rates reported: 1. Dispensed a systemic corticosteroid within 14 days of the event (PCE-1) 2. Dispensed a bronchodilator within 30 days of the event (PCE-2)

Time frame: Assessed at baseline, 6- and 12-Months

Population: Only a very small portion of the total sample was eligible for COPD Gaps in Care analyses.

ArmMeasureGroupCategoryValue (COUNT_OF_PARTICIPANTS)
High-TouchGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)Baseline PCE-1Did not close gap in care4 Participants
High-TouchGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)Baseline PCE-1Closed gap in care20 Participants
High-TouchGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)6-Month PCE-1Did not close gap in care8 Participants
High-TouchGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)6-Month PCE-1Closed gap in care49 Participants
High-TouchGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)12-Month PCE-1Did not close gap in care2 Participants
High-TouchGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)12-Month PCE-1Closed gap in care33 Participants
High-TouchGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)Baseline PCE-2Did not close gap in care8 Participants
High-TouchGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)Baseline PCE-2Closed gap in care16 Participants
High-TouchGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)6-Month PCE-2Did not close gap in care7 Participants
High-TouchGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)6-Month PCE-2Closed gap in care50 Participants
High-TouchGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)12-Month PCE-2Did not close gap in care5 Participants
High-TouchGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)12-Month PCE-2Closed gap in care30 Participants
High-TechGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)12-Month PCE-2Closed gap in care27 Participants
High-TechGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)Baseline PCE-1Did not close gap in care3 Participants
High-TechGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)Baseline PCE-2Did not close gap in care4 Participants
High-TechGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)6-Month PCE-2Did not close gap in care8 Participants
High-TechGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)Baseline PCE-1Closed gap in care25 Participants
High-TechGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)12-Month PCE-1Closed gap in care33 Participants
High-TechGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)12-Month PCE-2Did not close gap in care7 Participants
High-TechGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)6-Month PCE-1Did not close gap in care2 Participants
High-TechGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)Baseline PCE-2Closed gap in care24 Participants
High-TechGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)12-Month PCE-1Did not close gap in care1 Participants
High-TechGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)6-Month PCE-1Closed gap in care52 Participants
High-TechGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)6-Month PCE-2Closed gap in care46 Participants
Optimal Discharge PlanningGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)6-Month PCE-1Closed gap in care22 Participants
Optimal Discharge PlanningGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)12-Month PCE-1Did not close gap in care3 Participants
Optimal Discharge PlanningGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)6-Month PCE-2Closed gap in care20 Participants
Optimal Discharge PlanningGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)12-Month PCE-1Closed gap in care11 Participants
Optimal Discharge PlanningGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)Baseline PCE-2Did not close gap in care3 Participants
Optimal Discharge PlanningGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)Baseline PCE-2Closed gap in care12 Participants
Optimal Discharge PlanningGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)12-Month PCE-2Did not close gap in care1 Participants
Optimal Discharge PlanningGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)Baseline PCE-1Did not close gap in care1 Participants
Optimal Discharge PlanningGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)Baseline PCE-1Closed gap in care14 Participants
Optimal Discharge PlanningGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)6-Month PCE-2Did not close gap in care3 Participants
Optimal Discharge PlanningGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)6-Month PCE-1Did not close gap in care1 Participants
Optimal Discharge PlanningGaps in Care: Chronic Obstructive Pulmonary Disease (COPD)12-Month PCE-2Closed gap in care13 Participants
Comparison: Test of the marginal association between event rate and treatment for eligible participants' data at 12 months for PCE-1p-value: 0.1112Fisher Exact
Comparison: Test of the marginal association between event rate and treatment for eligible participants' data at 12 months for PCE-2p-value: 0.4754Fisher Exact
Secondary

Gaps in Care: Congestive Heart Failure (CHF)

For Gaps in care related to CHF, we assess readmission rate within 30 days after discharge from inpatient stay for members with a diagnosis of CHF prior index hospitalization.

Time frame: Assessed at 30-days from an index admission discharge.

Population: Only a small portion of the total sample was eligible for CHF Gaps in Care analyses.

ArmMeasureGroupValue (MEAN)Dispersion
High-TouchGaps in Care: Congestive Heart Failure (CHF)6-Month0.02 Number of readmissions within 30 daysStandard Deviation 0.1
High-TouchGaps in Care: Congestive Heart Failure (CHF)Baseline0.04 Number of readmissions within 30 daysStandard Deviation 0.17
High-TouchGaps in Care: Congestive Heart Failure (CHF)12-Month0.04 Number of readmissions within 30 daysStandard Deviation 0.13
High-TechGaps in Care: Congestive Heart Failure (CHF)6-Month0.04 Number of readmissions within 30 daysStandard Deviation 0.15
High-TechGaps in Care: Congestive Heart Failure (CHF)Baseline0.04 Number of readmissions within 30 daysStandard Deviation 0.17
High-TechGaps in Care: Congestive Heart Failure (CHF)12-Month0.04 Number of readmissions within 30 daysStandard Deviation 0.13
Optimal Discharge PlanningGaps in Care: Congestive Heart Failure (CHF)Baseline0.02 Number of readmissions within 30 daysStandard Deviation 0.11
Optimal Discharge PlanningGaps in Care: Congestive Heart Failure (CHF)12-Month0.04 Number of readmissions within 30 daysStandard Deviation 0.12
Optimal Discharge PlanningGaps in Care: Congestive Heart Failure (CHF)6-Month0.03 Number of readmissions within 30 daysStandard Deviation 0.12
Comparison: Test for the treatment effect (without weight) using a Firth's penalized logistic regression model for rare events.p-value: 0.907995% CI: [0.13, 4.28]Regression, Logistic
Comparison: Test for the treatment effect (without weight) using a Firth's penalized logistic regression model for rare events.p-value: 0.907995% CI: [0.13, 3.72]Regression, Logistic
Comparison: Test for the treatment effect (without weight) using a Firth's penalized logistic regression model for rare events.p-value: 0.907995% CI: [0.34, 3.45]Regression, Logistic
Secondary

Gaps in Care: Depression

Gaps in care will be assessed using Healthcare Effectiveness Data and Information Set (HEDIS) quality metrics. For depression, we assess the percentage of members diagnosed with a new episode of major depression, treated with antidepressant medication, and who remained on an antidepressant medication for: 1. Effective Acute Phase Treatment - 84 days of continuous treatment during 114-day period following the Index Prescription Start Date (AMM-1). 2. Effective Continuation Phase Treatment - 180 days of continuous treatment during 231-day period following the Index Prescription Start Date (AMM-2).

Time frame: Assessed at baseline, 6- and 12-Months

Population: Only a very small portion of the total sample was eligible for Depression Gaps in Care analyses.

ArmMeasureGroupCategoryValue (COUNT_OF_PARTICIPANTS)
High-TouchGaps in Care: DepressionBaseline AMM-1Did not close gap in care24 Participants
High-TouchGaps in Care: DepressionBaseline AMM-1Closed gap in care29 Participants
High-TouchGaps in Care: Depression6-Month AMM-1Did not close gap in care20 Participants
High-TouchGaps in Care: Depression6-Month AMM-1Closed gap in care35 Participants
High-TouchGaps in Care: Depression12-Month AMM-1Did not close gap in care22 Participants
High-TouchGaps in Care: Depression12-Month AMM-1Closed gap in care34 Participants
High-TouchGaps in Care: DepressionBaseline AMM-2Did not close gap in care28 Participants
High-TouchGaps in Care: DepressionBaseline AMM-2Closed gap in care25 Participants
High-TouchGaps in Care: Depression6-Month AMM-2Did not close gap in care26 Participants
High-TouchGaps in Care: Depression6-Month AMM-2Closed gap in care29 Participants
High-TouchGaps in Care: Depression12-Month AMM-2Did not close gap in care32 Participants
High-TouchGaps in Care: Depression12-Month AMM-2Closed gap in care24 Participants
High-TechGaps in Care: Depression12-Month AMM-2Closed gap in care28 Participants
High-TechGaps in Care: DepressionBaseline AMM-1Did not close gap in care12 Participants
High-TechGaps in Care: DepressionBaseline AMM-2Did not close gap in care19 Participants
High-TechGaps in Care: Depression6-Month AMM-2Did not close gap in care24 Participants
High-TechGaps in Care: DepressionBaseline AMM-1Closed gap in care35 Participants
High-TechGaps in Care: Depression12-Month AMM-1Closed gap in care37 Participants
High-TechGaps in Care: Depression12-Month AMM-2Did not close gap in care31 Participants
High-TechGaps in Care: Depression6-Month AMM-1Did not close gap in care14 Participants
High-TechGaps in Care: DepressionBaseline AMM-2Closed gap in care28 Participants
High-TechGaps in Care: Depression12-Month AMM-1Did not close gap in care22 Participants
High-TechGaps in Care: Depression6-Month AMM-1Closed gap in care30 Participants
High-TechGaps in Care: Depression6-Month AMM-2Closed gap in care20 Participants
Optimal Discharge PlanningGaps in Care: Depression6-Month AMM-1Closed gap in care19 Participants
Optimal Discharge PlanningGaps in Care: Depression12-Month AMM-1Did not close gap in care9 Participants
Optimal Discharge PlanningGaps in Care: Depression6-Month AMM-2Closed gap in care16 Participants
Optimal Discharge PlanningGaps in Care: Depression12-Month AMM-1Closed gap in care25 Participants
Optimal Discharge PlanningGaps in Care: DepressionBaseline AMM-2Did not close gap in care16 Participants
Optimal Discharge PlanningGaps in Care: DepressionBaseline AMM-2Closed gap in care7 Participants
Optimal Discharge PlanningGaps in Care: Depression12-Month AMM-2Did not close gap in care14 Participants
Optimal Discharge PlanningGaps in Care: DepressionBaseline AMM-1Did not close gap in care10 Participants
Optimal Discharge PlanningGaps in Care: DepressionBaseline AMM-1Closed gap in care13 Participants
Optimal Discharge PlanningGaps in Care: Depression6-Month AMM-2Did not close gap in care9 Participants
Optimal Discharge PlanningGaps in Care: Depression6-Month AMM-1Did not close gap in care6 Participants
Optimal Discharge PlanningGaps in Care: Depression12-Month AMM-2Closed gap in care20 Participants
Comparison: Test for the treatment-by-time interaction (without weight) for AMM-1p-value: 0.8247Mixed Models Analysis
Comparison: Test for change over time for AMM-1p-value: 0.6651Mixed Models Analysis
Comparison: Test for change in treatment effect for AMM-1p-value: 0.3441Mixed Models Analysis
Comparison: Test for the treatment-by-time interaction (without weight) for AMM-2p-value: 0.0847Mixed Models Analysis
Comparison: Test for change over time for AMM-2p-value: 0.9695Mixed Models Analysis
Comparison: Test for change in treatment effect for AMM-2p-value: 0.9396Mixed Models Analysis
Secondary

Gaps in Care: Diabetes

Gaps in care will be assessed using Healthcare Effectiveness Data and Information Set (HEDIS) quality metrics. For diabetes, we assess the percentage of members 40-75 years of age during the measurement year with diabetes who do not have clinical atherosclerotic cardiovascular disease (ASCVD) who met the following criteria. Two rates are reported: 1. Received Statin Therapy. Members who were dispensed at least one statin medication of any intensity during the measurement year (SPD-1). 2. Statin Adherence 80%. Members who remained on a statin medication of any intensity for at least 80% of the treatment period (SPD-2).

Time frame: Assessed at baseline, 6- and 12-Months

Population: Only a very small portion of the total sample was eligible for Diabetes Gaps in Care analyses.

ArmMeasureGroupCategoryValue (COUNT_OF_PARTICIPANTS)
High-TouchGaps in Care: DiabetesBaseline SPD-1Did not close gap in care25 Participants
High-TouchGaps in Care: DiabetesBaseline SPD-1Closed gap in care80 Participants
High-TouchGaps in Care: Diabetes6-Month SPD-1Did not close gap in care28 Participants
High-TouchGaps in Care: Diabetes6-Month SPD-1Closed gap in care79 Participants
High-TouchGaps in Care: Diabetes12-Month SPD-1Did not close gap in care20 Participants
High-TouchGaps in Care: Diabetes12-Month SPD-1Closed gap in care68 Participants
High-TouchGaps in Care: DiabetesBaseline SPD-2Did not close gap in care19 Participants
High-TouchGaps in Care: DiabetesBaseline SPD-2Closed gap in care61 Participants
High-TouchGaps in Care: Diabetes6-Month SPD-2Did not close gap in care18 Participants
High-TouchGaps in Care: Diabetes6-Month SPD-2Closed gap in care61 Participants
High-TouchGaps in Care: Diabetes12-Month SPD-2Did not close gap in care14 Participants
High-TouchGaps in Care: Diabetes12-Month SPD-2Closed gap in care54 Participants
High-TechGaps in Care: Diabetes12-Month SPD-2Closed gap in care43 Participants
High-TechGaps in Care: DiabetesBaseline SPD-1Did not close gap in care24 Participants
High-TechGaps in Care: DiabetesBaseline SPD-2Did not close gap in care31 Participants
High-TechGaps in Care: Diabetes6-Month SPD-2Did not close gap in care27 Participants
High-TechGaps in Care: DiabetesBaseline SPD-1Closed gap in care95 Participants
High-TechGaps in Care: Diabetes12-Month SPD-1Closed gap in care69 Participants
High-TechGaps in Care: Diabetes12-Month SPD-2Did not close gap in care26 Participants
High-TechGaps in Care: Diabetes6-Month SPD-1Did not close gap in care25 Participants
High-TechGaps in Care: DiabetesBaseline SPD-2Closed gap in care64 Participants
High-TechGaps in Care: Diabetes12-Month SPD-1Did not close gap in care19 Participants
High-TechGaps in Care: Diabetes6-Month SPD-1Closed gap in care79 Participants
High-TechGaps in Care: Diabetes6-Month SPD-2Closed gap in care52 Participants
Optimal Discharge PlanningGaps in Care: Diabetes6-Month SPD-1Closed gap in care33 Participants
Optimal Discharge PlanningGaps in Care: Diabetes12-Month SPD-1Did not close gap in care9 Participants
Optimal Discharge PlanningGaps in Care: Diabetes6-Month SPD-2Closed gap in care22 Participants
Optimal Discharge PlanningGaps in Care: Diabetes12-Month SPD-1Closed gap in care25 Participants
Optimal Discharge PlanningGaps in Care: DiabetesBaseline SPD-2Did not close gap in care18 Participants
Optimal Discharge PlanningGaps in Care: DiabetesBaseline SPD-2Closed gap in care26 Participants
Optimal Discharge PlanningGaps in Care: Diabetes12-Month SPD-2Did not close gap in care6 Participants
Optimal Discharge PlanningGaps in Care: DiabetesBaseline SPD-1Did not close gap in care15 Participants
Optimal Discharge PlanningGaps in Care: DiabetesBaseline SPD-1Closed gap in care44 Participants
Optimal Discharge PlanningGaps in Care: Diabetes6-Month SPD-2Did not close gap in care11 Participants
Optimal Discharge PlanningGaps in Care: Diabetes6-Month SPD-1Did not close gap in care12 Participants
Optimal Discharge PlanningGaps in Care: Diabetes12-Month SPD-2Closed gap in care19 Participants
Comparison: Test for the treatment-by-time interaction (without weight) for SPD-1.p-value: 0.9786Mixed Models Analysis
Comparison: Test for change over time for SPD-1p-value: 0.4703Mixed Models Analysis
Comparison: Test for change in treatment effect for SPD-1p-value: 0.877Mixed Models Analysis
Comparison: Test for the treatment-by-time interaction (without weight) for SPD-2p-value: 0.6198Mixed Models Analysis
Comparison: Test for change over time for SPD-2p-value: 0.6211Mixed Models Analysis
Comparison: Test for change in treatment effect for SPD-2.p-value: 0.1471Mixed Models Analysis
Secondary

Inpatient Readmissions Over 12-Months

Readmissions over 12 months will be measured using an all-cause readmission rate from inpatient claims for physical and behavioral health service use within one year following discharge from the qualifying inpatient admission prior to enrollment in the study. Inpatient readmissions were lower than hypothesized for the population. As such, we assessed a Y/N variable for inpatient readmissions at 12-Months.

Time frame: Assessed at baseline, 6- and 12-Months.

Population: For each timepoint, participants who were eligible for Medicaid/Medicaid-Medicare with available claims data during at least 9 months (non-continuous) of the past 12 months.

ArmMeasureGroupCategoryValue (COUNT_OF_PARTICIPANTS)
High-TouchInpatient Readmissions Over 12-Months6-MonthNo inpatient admission412 Participants
High-TouchInpatient Readmissions Over 12-Months12-MonthMissing Data30 Participants
High-TouchInpatient Readmissions Over 12-Months6-MonthMissing Data8 Participants
High-TouchInpatient Readmissions Over 12-Months6-MonthOne or more inpatient admissions139 Participants
High-TouchInpatient Readmissions Over 12-MonthsBaselineNo inpatient admission342 Participants
High-TouchInpatient Readmissions Over 12-Months12-MonthOne or more inpatient admissions192 Participants
High-TouchInpatient Readmissions Over 12-MonthsBaselineMissing Data44 Participants
High-TouchInpatient Readmissions Over 12-MonthsBaselineOne or more inpatient admissions173 Participants
High-TouchInpatient Readmissions Over 12-Months12-MonthNo inpatient admission337 Participants
High-TechInpatient Readmissions Over 12-Months6-MonthOne or more inpatient admissions135 Participants
High-TechInpatient Readmissions Over 12-MonthsBaselineNo inpatient admission344 Participants
High-TechInpatient Readmissions Over 12-MonthsBaselineOne or more inpatient admissions165 Participants
High-TechInpatient Readmissions Over 12-MonthsBaselineMissing Data36 Participants
High-TechInpatient Readmissions Over 12-Months6-MonthNo inpatient admission400 Participants
High-TechInpatient Readmissions Over 12-Months6-MonthMissing Data10 Participants
High-TechInpatient Readmissions Over 12-Months12-MonthNo inpatient admission332 Participants
High-TechInpatient Readmissions Over 12-Months12-MonthOne or more inpatient admissions188 Participants
High-TechInpatient Readmissions Over 12-Months12-MonthMissing Data25 Participants
Optimal Discharge PlanningInpatient Readmissions Over 12-MonthsBaselineMissing Data29 Participants
Optimal Discharge PlanningInpatient Readmissions Over 12-MonthsBaselineNo inpatient admission170 Participants
Optimal Discharge PlanningInpatient Readmissions Over 12-Months12-MonthNo inpatient admission170 Participants
Optimal Discharge PlanningInpatient Readmissions Over 12-MonthsBaselineOne or more inpatient admissions84 Participants
Optimal Discharge PlanningInpatient Readmissions Over 12-Months12-MonthMissing Data23 Participants
Optimal Discharge PlanningInpatient Readmissions Over 12-Months6-MonthOne or more inpatient admissions73 Participants
Optimal Discharge PlanningInpatient Readmissions Over 12-Months6-MonthNo inpatient admission201 Participants
Optimal Discharge PlanningInpatient Readmissions Over 12-Months12-MonthOne or more inpatient admissions90 Participants
Optimal Discharge PlanningInpatient Readmissions Over 12-Months6-MonthMissing Data9 Participants
Comparison: Overall test for the treatment-by-time interactionp-value: 0.9804Mixed Models Analysis
Comparison: Test for the change over time when the treatment-by-time interaction effect is not significant.p-value: <0.0001Mixed Models Analysis
Comparison: Test for the change by treatment when the treatment-by-time interaction effect is not significant.p-value: 0.9764Mixed Models Analysis
Comparison: Odds ratio of Treatment at 12-Monthsp-value: 0.975795% CI: [0.63, 1.6]Mixed Models Analysis
Comparison: Odds ratio of Treatment at 12-Monthsp-value: 0.795895% CI: [0.67, 1.68]Mixed Models Analysis
Comparison: Odds ratio of Treatment at 12-Monthsp-value: 0.764695% CI: [0.67, 1.34]Mixed Models Analysis
Secondary

Mental Health Care Visits

Assessed using existing behavioral health claims data determining frequency of mental health care visits for participants in the 12 months following enrollment. Because of the low frequency, we assess mental health care visits as a Y/N variable.

Time frame: Assessed at baseline, 6- and 12-Months.

Population: For each timepoint, participants who were eligible for Medicaid/Medicaid-Medicare with available claims data during at least 9 months (non-continuous) of the past 12 months.

ArmMeasureGroupCategoryValue (COUNT_OF_PARTICIPANTS)
High-TouchMental Health Care Visits6-MonthsNo mental health care visits527 Participants
High-TouchMental Health Care Visits12-MonthsMissing Data30 Participants
High-TouchMental Health Care Visits6-MonthsMissing Data8 Participants
High-TouchMental Health Care Visits6-MonthsAt least one mental health care visit24 Participants
High-TouchMental Health Care VisitsBaselineNo mental health care visits479 Participants
High-TouchMental Health Care Visits12-MonthsAt least one mental health care visit37 Participants
High-TouchMental Health Care VisitsBaselineMissing Data44 Participants
High-TouchMental Health Care VisitsBaselineAt least one mental health care visit36 Participants
High-TouchMental Health Care Visits12-MonthsNo mental health care visits492 Participants
High-TechMental Health Care Visits6-MonthsAt least one mental health care visit29 Participants
High-TechMental Health Care VisitsBaselineNo mental health care visits476 Participants
High-TechMental Health Care VisitsBaselineAt least one mental health care visit33 Participants
High-TechMental Health Care VisitsBaselineMissing Data36 Participants
High-TechMental Health Care Visits6-MonthsNo mental health care visits506 Participants
High-TechMental Health Care Visits6-MonthsMissing Data10 Participants
High-TechMental Health Care Visits12-MonthsNo mental health care visits484 Participants
High-TechMental Health Care Visits12-MonthsAt least one mental health care visit36 Participants
High-TechMental Health Care Visits12-MonthsMissing Data25 Participants
Optimal Discharge PlanningMental Health Care VisitsBaselineMissing Data29 Participants
Optimal Discharge PlanningMental Health Care VisitsBaselineNo mental health care visits237 Participants
Optimal Discharge PlanningMental Health Care Visits12-MonthsNo mental health care visits238 Participants
Optimal Discharge PlanningMental Health Care VisitsBaselineAt least one mental health care visit17 Participants
Optimal Discharge PlanningMental Health Care Visits12-MonthsMissing Data23 Participants
Optimal Discharge PlanningMental Health Care Visits6-MonthsAt least one mental health care visit18 Participants
Optimal Discharge PlanningMental Health Care Visits6-MonthsNo mental health care visits256 Participants
Optimal Discharge PlanningMental Health Care Visits12-MonthsAt least one mental health care visit22 Participants
Optimal Discharge PlanningMental Health Care Visits6-MonthsMissing Data9 Participants
Comparison: Overall test for the treatment-by-time interactionp-value: 0.9622Mixed Models Analysis
Comparison: Test for the change over time when the treatment-by-time interaction effect is not significant.p-value: 0.0377Mixed Models Analysis
Comparison: Test for the change by treatment when the treatment-by-time interaction effect is not significant.p-value: 0.83Mixed Models Analysis
Comparison: Odds ratio of Treatment at 12-Monthsp-value: 0.750795% CI: [0.38, 2]Mixed Models Analysis
Comparison: Odds ratio of Treatment at 12-Monthsp-value: 0.884495% CI: [0.42, 2.13]Mixed Models Analysis
Comparison: Odds ratio of Treatment at 12-Monthsp-value: 0.822795% CI: [0.49, 1.77]Mixed Models Analysis
Secondary

Quality of Life

Quality of Life will be assessed using the Quality of Life Enjoyment and Satisfaction Questionnaire - Short Form (Q-LES-Q-SF), which is a self-report measure consisting of 16 questions designed to enable investigators to easily obtain sensitive measures of the degree of enjoyment and satisfaction experienced by subjects in various areas of daily functioning during the past week. The scoring of the Q-LES-Q-SF involves summing only the first 14 items to yield a raw total score, ranging from 14 to 70. The raw total score is calculated into a maximum possible score using the following formula: (raw total score - minimum score)/(maximum possible raw score - minimum score). The minimum raw score on the Q-LES-Q-SF is 14, and the maximum score is 70. Thus, the formula for maximum score can also be written as: (raw score - 14)/56.

Time frame: Baseline, 3-, 6-, and 12-months.

Population: Includes all individuals who completed the measure at least one timepoint.

ArmMeasureGroupValue (MEAN)Dispersion
High-TouchQuality of Life12-Month0.54 score on a scaleStandard Deviation 0.2
High-TouchQuality of Life6-Month0.52 score on a scaleStandard Deviation 0.2
High-TouchQuality of LifeBaseline0.50 score on a scaleStandard Deviation 0.19
High-TouchQuality of Life3-Month0.52 score on a scaleStandard Deviation 0.19
High-TechQuality of LifeBaseline0.51 score on a scaleStandard Deviation 0.19
High-TechQuality of Life3-Month0.52 score on a scaleStandard Deviation 0.19
High-TechQuality of Life6-Month0.52 score on a scaleStandard Deviation 0.2
High-TechQuality of Life12-Month0.52 score on a scaleStandard Deviation 0.19
Optimal Discharge PlanningQuality of Life6-Month0.52 score on a scaleStandard Deviation 0.21
Optimal Discharge PlanningQuality of LifeBaseline0.51 score on a scaleStandard Deviation 0.21
Optimal Discharge PlanningQuality of Life3-Month0.52 score on a scaleStandard Deviation 0.2
Optimal Discharge PlanningQuality of Life12-Month0.53 score on a scaleStandard Deviation 0.19
Comparison: Overall test of treatment-by-timep-value: 0.5199Mixed Models Analysis
Comparison: Test for the change over time when the treatment-by-time interaction effect is not significant.p-value: 0.0008Mixed Models Analysis
Comparison: Test for the change by treatment when the treatment-by-time interaction effect is not significant.p-value: 0.9897Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12 months using the linear contrasts.p-value: 0.874995% CI: [-0.03, 0.03]Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12 months using the linear contrasts.p-value: 0.221695% CI: [-0.05, 0.01]Mixed Models Analysis
Comparison: Test for Group difference in the change from baseline to 12 months using the linear contrasts.p-value: 0.060795% CI: [0, 0.04]Mixed Models Analysis

Source: ClinicalTrials.gov · Data processed: Feb 16, 2026