Health behavior change in advance care planning: An agent-based model

Significance: A practical and ethical challenge in advance care planning research is controlling and intervening on human behavior. Additionally, observing dynamic changes in advance care planning (ACP) behavior proves difficult, though tracking changes over time is important for intervention development. Agent-based modeling (ABM) allows researchers to integrate complex behavioral data about advance care planning behaviors and thought processes into a controlled environment that is more easily alterable and observable. Literature to date has not addressed how best to motivate individuals, increase facilitators and reduce barriers associated with ACP. We aimed to build an ABM that accurately reflects: 1) the rates at which individuals complete the ACP process, 2) how individuals respond to barriers, facilitators, and behavioral variables 3) the interactions between these variables, 4) suggests -future -public health interventions and validation studies. Methods: We developed an ABM of the ACP -decision making process. We integrated into this dynamic model the barriers, facilitators, and other behavioral variables - that -agents encounter as they move- through the Transtheoretical Model’s stages of change. Findings: We successfully incorporated ACP barriers, facilitators, and other behavioral variables into our ABM, forming a plausible representation of ACP behavior and decision-making. In addition, the resulting distributions across the stages of change replicated those found in the literature, with approximately half of participants in the action-maintenance stage in both the model and the literature. Public Health Implications: Our ABM is the first of its kind to outline potential intervention points for behavior change in the context of ACP. The ABM approach to ACP is a useful method for representing dynamic social and experiential influences on the decision making process. This model could be used in the future to test structural interventions (e.g. increasing access to ACP materials in primary care clinics) theoretically before implementation. Future studies can expand on this by gathering longitudinal, individual-level data and integrating it into the ABM for a more comprehensive representation of decision-making patterns with respect to ACP

Health behavior change in advance care planning: an agent-based model

Abstract Background A practical and ethical challenge in advance care planning research is controlling and intervening on human behavior. Additionally, observing dynamic changes in advance care planning (ACP) behavior proves difficult, though tracking changes over time is important for intervention development. Agent-based modeling (ABM) allows researchers to integrate complex behavioral data about advance care planning behaviors and thought processes into a controlled environment that is more easily alterable and observable. Literature to date has not addressed how best to motivate individuals, increase facilitators and reduce barriers associated with ACP. We aimed to build an ABM that applies the Transtheoretical Model of behavior change to ACP as a health behavior and accurately reflects: 1) the rates at which individuals complete the process, 2) how individuals respond to barriers, facilitators, and behavioral variables, and 3) the interactions between these variables. Methods We developed a dynamic ABM of the ACP decision making process based on the stages of change posited by the Transtheoretical Model. We integrated barriers, facilitators, and other behavioral variables that agents encounter as they move through the process. Results We successfully incorporated ACP barriers, facilitators, and other behavioral variables into our ABM, forming a plausible representation of ACP behavior and decision-making. The resulting distributions across the stages of change replicated those found in the literature, with approximately half of participants in the action-maintenance stage in both the model and the literature. Conclusions Our ABM is a useful method for representing dynamic social and experiential influences on the ACP decision making process. This model suggests structural interventions, e.g. increasing access to ACP materials in primary care clinics, in addition to improved methods of data collection for behavioral studies, e.g. incorporating longitudinal data to capture behavioral dynamics

Does Receipt of Recommended Elements of Palliative Care Precede In-Hospital Death or Hospice Referral?

Context. Palliative care aligns treatments with patients’ values and improves quality of life, yet whether receipt of recommended elements of palliative care is associated with end-of-life outcomes is understudied. Objectives. To assess whether recommended elements of palliative care (pain and symptom management, goals of care, and spiritual care) precede in-hospital death and hospice referral and whether delivery by specialty palliative care affects that relationship. Methods. We conducted structured chart reviews for decedents with late-stage cancer, dementia, and chronic kidney disease with a hospital admission during the six months preceding death. Measures included receipt of recommended elements of palliative care delivered by any clinician and specialty palliative care consult. We assessed associations between recommended elements of palliative care and in-hospital death and hospice referral using multivariable Poisson regression models. Results. Of 402 decedents, 67 (16.7%) died in hospital, and 168 (41.8%) had hospice referral. Among elements of palliative care, only goals-of-care discussion was associated with in-hospital death (incidence rate ratio [IRR] 1.37; 95% CI 1.01e1.84) and hospice referral (IRR 1.85; 95% CI 1.31e2.61). Specialty palliative care consult was associated with a lower likelihood of in-hospital death (IRR 0.57; 95% CI 0.44e0.73) and a higher likelihood of hospice referral (IRR 1.45; 95% CI 1.12e1.89) compared with no consult. Conclusion. Goals-of-care discussions by different types of clinicians commonly precede end-of-life care in hospital or hospice. However, engagement with specialty palliative care reduced in-hospital death and increased hospice referral. Understanding the causal pathways of goals-of-care discussions may help build primary palliative care interventions to support patients near the end of life

Elements of Palliative Care in the Last 6 Months of Life: Frequency, Predictors, and Timing

IMPORTANCE: Persons living with serious illness often need skilled symptom management, communication, and spiritual support. Palliative care addresses these needs and may be delivered by either specialists or clinicians trained in other fields. It is important to understand core elements of palliative care to best provide patient-centered care. OBJECTIVE: To describe frequency, predictors, and timing of core elements of palliative care during the last 6 months of life. DESIGN: Retrospective chart review. SETTING: Inpatient academic medical center. PARTICIPANTS: Decedents with cancer, dementia, or chronic kidney disease (CKD) admitted during the 6 months preceding death. EXPOSURES: We identified receipt and timing of core elements of palliative care: pain and symptom management, goals of care, spiritual care; and specialty palliative care utilization; hospital encounters; demographics; and comorbid diagnoses.We ran Poisson regression models to assess whether diagnosis or hospital encounters were associated with core elements of palliative care. RESULTS: Among 402 decedents, themean (SD) number of appropriately screened and treated symptoms was 2.9 (1.7)/10. Among 76.1% with documented goals of care, 58.0% had a primary goal of comfort; 55.0% had documented spiritual care. In multivariable models, compared with decedents with cancer, those with dementia or CKD were less likely to have pain and symptom management (respectively, 31% (incidence rate ratio [IRR], 0.69; 95% CI, 0.56–0.85) and 17% (IRR, 0.83; CI, 0.71–0.97)). There was amedian of 3 days (IQR, 0–173) between transition to a goal of comfort and death, and amedian of 12 days (IQR, 5–47) between hospice referral and death. CONCLUSIONS AND RELEVANCE: Although a high proportion of patients received elements of palliative care, transitions to a goal of comfort or hospice happened very near death. Palliative care delivery can be improved by systematizing existing mechanisms, including prompts for earlier goals-of-care discussion, symptom screening, and spiritual care, and by building collaboration between primary and specialty palliative care services

Triggered Palliative Care for Late-stage Dementia: a Pilot Randomized Trial

Context Persons with late-stage dementia have limited access to palliative care. Objective To test dementia-specific specialty palliative care triggered by hospitalization. Methods This pilot randomized controlled trial enrolled 62 dyads of persons with late-stage dementia and family decision-makers on admission to hospital. Intervention dyads received dementia-specific specialty palliative care consultation plus post-acute transitional care. Control dyads received usual care and educational information. The primary outcome was 60-day hospital or emergency department visits. Secondary patient and family-centered outcomes were patient comfort, family distress, palliative care domains addressed in the treatment plan, and access to hospice or community-based palliative care. Secondary decision-making outcomes were discussion of prognosis, goals of care, completion of Medical Orders for Scope of Treatment (MOST), and treatment decisions. Results Of 137 eligible dyads, 62 (45%) enrolled. The intervention proved feasible, with protocol completion ranging from 77% (family 2-week call) to 93% (initial consultation). Hospital and emergency department visits did not differ (intervention vs control, 0.68 vs 0.53 transfers per 60 days, p=0.415). Intervention patients had more palliative care domains addressed, and were more likely to receive hospice (25% vs 3%, p<0.019). Intervention families were more likely to discuss prognosis (90% vs 3%, p<0.001) and goals of care (90% vs 25%, p<0.001), and to have a MOST at 60-day follow-up (79% vs 30%, p<0.001). More intervention families made decisions to avoid re-hospitalization (13% vs 0%, p=0.033). Conclusion Specialty palliative care consultation for hospitalized patients with for late-stage dementia is feasible and promising to improve decision-making and some treatment outcomes

Electronic Health Record Phenotypes for Identifying Patients with Late-Stage Disease: a Method for Research and Clinical Application

BACKGROUND: Systematic identification of patients allows researchers and clinicians to test new models of care delivery. EHR phenotypes—structured algorithms based on clinical indicators from EHRs—can aid in such identification. OBJECTIVE: To develop EHR phenotypes to identify decedents with stage 4 solid-tumor cancer or stage 4–5 chronic kidney disease (CKD). DESIGN: We developed two EHR phenotypes. Each phenotype included International Classification of Diseases (ICD)-9 and ICD-10 codes. We used natural language processing (NLP) to further specify stage 4 cancer, and lab values for CKD. SUBJECTS: Decedents with cancer or CKD who had been admitted to an academic medical center in the last 6 months of life and died August 26, 2017–December 31, 2017. MAIN MEASURE: We calculated positive predictive values (PPV), false discovery rates (FDR), false negative rates (FNR), and sensitivity. Phenotypes were validated by a comparison with manual chart review. We also compared the EHR phenotype results to those admitted to the oncology and nephrology inpatient services. KEY RESULTS: The EHR phenotypes identified 271 decedents with cancer, of whom 186 had stage 4 disease; of 192 decedents with CKD, 89 had stage 4–5 disease. The EHR phenotype for stage 4 cancer had a PPV of 68.6%, FDR of 31.4%, FNR of 0.5%, and 99.5% sensitivity. The EHR phenotype for stage 4–5 CKD had a PPV of 46.4%, FDR of 53.7%, FNR of 0.0%, and 100% sensitivity. CONCLUSIONS: EHR phenotypes efficiently identified patients who died with late-stage cancer or CKD. Future EHR phenotypes can prioritize specificity over sensitivity, and incorporate stratification of high- and low-palliative care need. EHR phenotypes are a promising method for identifying patients for research and clinical purposes, including equitable distribution of specialty palliative care

Association Between Palliative Care and Patient and Caregiver Outcomes: A Systematic Review and Meta-analysis

The use of palliative care programs and the number of trials assessing their effectiveness have increased

Cognitive and behavioral predictors of light therapy use

Objective: Although light therapy is effective in the treatment of seasonal affective disorder (SAD) and other mood disorders, only 53-79% of individuals with SAD meet remission criteria after light therapy. Perhaps more importantly, only 12-41% of individuals with SAD continue to use the treatment even after a previous winter of successful treatment. Method: Participants completed surveys regarding (1) social, cognitive, and behavioral variables used to evaluate treatment adherence for other health-related issues, expectations and credibility of light therapy, (2) a depression symptoms scale, and (3) self-reported light therapy use. Results: Individuals age 18 or older responded (n = 40), all reporting having been diagnosed with a mood disorder for which light therapy is indicated. Social support and self-efficacy scores were predictive of light therapy use (p's<.05). Conclusion: The findings suggest that testing social support and self-efficacy in a diagnosed patient population may identify factors related to the decision to use light therapy. Treatments that impact social support and self-efficacy may improve treatment response to light therapy in SAD. © 2012 Roecklein et al