Journal of comorbidity最新文献

Background: Continuity of care is a basic tenant of primary care practice. However, the evidence on the importance of continuity of care for older adults with complex conditions is mixed.

Objective: To assess the relationship between measurement of continuity of care, number of chronic conditions, and health outcomes.

Design: We analyzed data from a cohort of 1,600 US older adults with diabetes and ≥1 other chronic condition in a private Medicare health plan from July 2010 to December 2011. Multivariate regression models were used to examine the association of baseline continuity (the first 6 months) and the composite outcome of any emergency room use or inpatient hospitalization occurring in the following 12-month period.

Results: After adjusting for baseline covariates, high known provider continuity (KPC) was associated with an 84% (adjusted odds ratio 0.16; 95% confidence interval 0.09-0.26) reduction in the risk of the composite outcome. High KPC was significantly associated with a lower risk of the composite outcome among individuals with ≥6 conditions. However, the usual provider of care and continuity of care indices were not significantly related with the composite outcome in the overall sample or in those with ≥6 conditions.

Conclusion: The relationship between continuity of care and adverse outcomes depends on the measure of continuity of care employed. High morbidity patients are more likely to benefit from continuity of care interventions as measured by the KPC, which measures the proportion of a patient's visits that are with the same providers over time.

Background: Psychiatric syndromes are complicated by comorbidity and other factors that burden patients, making guideline-informed psychiatric care challenging, and negatively affecting outcome. A comprehensive intake tool could improve the quality of care. Existing tools to quantify these characteristics do not identify specific complications and may not be sensitive to phenomena that are common in psychiatric outpatients.

Objective: To develop a practical inventory to capture observations related to complex care in psychiatric outpatients and quantify the overall burden of complicating factors.

Design: We developed a checklist inventory through literature review and clinical experience. The inventory was tested and compared with related measures in a cross-sectional study of 410 consenting outpatients at the time of initial assessment.

Results: The summed score of inventory checklist items was significantly correlated with patient-assessed measures of distress (K10, r=0.36) and function (WHODAS 2.0, r=0.31), and physician-assessed measures of function (GAF, r=-0.42), number of psychiatric diagnoses [F(df3)=33.6], and most complex diagnosis [F(df3)=37.4]. In 53 patients whose assessment was observed by two clinicians, inter-rater reliability was acceptable for both total inventory score (intraclass correlation, single measures = 0.74) and agreement on specific items (mean agreement score = 90%).

Conclusions: The Psychiatric C4 Inventory is a reliable instrument for psychiatrists that captures information that may be useful for quality improvement and resource planning. It demonstrates convergent validity with measures of patient distress, function, and complexity. Further tests of validity and replication in other settings are warranted.

Background: Approximately 80% of people who survive a stroke have on average five other conditions and a wide range of psychosocial issues. Attention to biopsychosocial issues has led to the identification of 'complex patients'. No single definition of 'patient complexity' exists; therefore, applied health researchers seek to understand 'patient complexity' as it relates to a specific clinical context.

Objective: To understand how 'patient complexity' is conceptualized by clinicians, and to position the findings within the existing literature on patient complexity.

Methods: A qualitative descriptive approach was utilized. Twenty-three stroke rehabilitation clinicians participated in four focus groups.

Results: Five elements of patient complexity were identified: medical/functional issues, social determinant factors, social/family support, personal characteristics, and health system factors. Using biopsychosocial factors to identify complexity results in all patients being complex; operationalization of the definition led to the identification of systemic elements. A disconnect between acute, inpatient rehabilitation and community services was identified as a trigger for increased complexity.

Conclusions: Patient complexity is not a dichotomous state. If applying existing complexity definitions, all patients are complex. This study extends the understanding by suggesting a structural element of complexity from manageable to less manageable complexity based on ability to discharge.

[This corrects the article DOI: 10.15256/joc.2016.6.74.].

[This corrects the article DOI: 10.15256/joc.2016.6.73.].