Ensuring that Veterans with serious illness receive patient-centered care is a fundamental goal of medical care. Advanced liver disease (AdvLD) is a serious illness that disproportionately affects Veterans and carries high rates of morbidity and mortality. As many as half of AdvLD patients develop liver complications (e.g., ascites, encephalopathy, or liver cancer) within 5 years of diagnosis; half of these patients die within 2 years of developing complications. Despite notable advances in the care of patients with AdvLD, many gaps remain. Liver transplantation offers an opportunity to cure AdvLD, however, few patients receive it; most patients, even those on the transplant waiting list, live with and die of their AdvLD. Their clinical course is marked by declining health, increasing symptom burden and frequent hospitalizations. However, supportive care for AdvLD is often episodic and reactive, and palliative care is rare.
Recent patient-centered models of care in other conditions, like cancer, have promoted early integration of supportive, palliative and curative care. These models can improve quality and even length of life. Integrated care models may be even more important in non-cancer conditions such as AdvLD where disease course and care trajectories can be more prolonged and uncertain, yet much can be done with patients to reduce complications and maintain both function and well-being.
There are several reasons why patient-centered integrated models for AdvLD have been slow to develop. First, a precise and complete understanding of disease severity and progression is lacking. Existing prognostic scores, like Model for End Stage Liver Disease (MELD) or Child Pugh are disease-specific and do not account for a wide range of psychosocial and clinical factors that are likely to be associated with suboptimal outcomes in AdvLD. Incorporation of non-liver disease factors in prognostication is especially important for VA patients, as many are impoverished and have multiple comorbidities, often with drug or alcohol use disorders. Conversely, the general prognostic models that take psychosocial and clinical factors into account (such as Charlson or Care Assessment Need score) do not incorporate liver disease severity and miss AdvLD-specific outcomes. Second, Veterans' experiences with VA health care likely shape how they access and engage in AdvLD care. However, there have been no studies characterizing Veterans' illness-specific health care needs, their understanding of illness severity, as well as the goals of care that matter to them across the spectrum of disease severity. Third, although clinicians' current approaches to supportive care for AdvLD are the starting point for developing more patient-centered models, little is known about their experiences, expectations, and perceived barriers to delivering AdvLD care.
To use a multi-method approach to fill knowledge gaps in the care of advanced liver disease (AdvLD) patients that are crucial to integrated care for AdvLD.
Aim 1: To develop risk stratification models of advanced liver disease prognosis.
Aim 2: To describe Veteran patients' experiences and goals of advanced liver disease care.
Aim 3:To identify clinicians' perceptions of opportunities and barriers to patient-centered advanced liver disease care.
Aim 1: To develop risk stratification models of AdvLD prognosis. We will develop models that combine liver severity indices (e.g., MELD, Child Pugh) with psychosocial (e.g., age, race, homelessness), clinical (e.g., physical and mental health comorbidity, alcohol use), and healthcare resource use (e.g., emergency room visits) factors to estimate the risk of developing AdvLD complications, requiring AdvLD related hospitalizations, and overall mortality. We will use existing automated data from a national cohort of Veterans with AdvLD (n~45,000) enrolled in the VA between 2011 and 2015. Aim 1 will provide estimates of AdvLD Veterans' risk (e.g., low, intermediate or high) of developing AdvLD-specific outcomes. These data will allow patients, caregivers and clinicians to understand patients' illness severity and future risks.
Aim 2: To describe patients' experiences and goals of AdvLD care. We will conduct in-depth interviews with a representative sample of AdvLD patients (and their caregivers) from three VA centers to understand patients' experiences with care; perceptions of illness severity including preferences for the amount and type of risk information needed; and desired health outcome goals. Aim 2 will identify the range of goals that patients in different risk strata perceive as important. Data from Aims 1 and 2 will allow clinicians to place their patients' perspectives in context and deliver clinical care consistent with patients' prognosis and goals.
Aim 3: To identify clinicians' perceptions of opportunities and barriers to patient-centered AdvLD care. We will conduct in-depth interviews with clinicians involved with AdvLD treatment planning at three VA centers to examine their experiences in communicating risk and making treatment plans; perceptions of their and patients' roles in treatment planning; and barriers to and facilitators of providing care aligned with patients' goals. Aim 3 will complement the first two by identifying problems that clinicians face delivering patient-centered AdvLD care as well as possible solutions to these problems. Combined with Aim 2, data from Aim 3 will provide design elements for intervention strategies to improve patient-centered AdvLD care.
There are no findings at this time.
This study will develop a patient-centered model of collaborative care for AdvLD that may be applicable to other serious illness conditions. It will develop Veteran-specific risk stratification models for AdvLD care that will provide clinicians with a practical method for predicting risks of major clinical outcomes in AdvLD.
Interviews with Veterans and their caregivers will reveal how patients with AdvLD experience care and to what extent such care meets their needs and preferences.
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Health Systems, Kidney Disorders
Technology Development and Assessment, TRL - Development
Healthcare Algorithms, Outcomes - Patient, Patient Preferences, Patient-Provider Interaction, Predictive Modeling