An Evidence-Based Care Model for Workers With Concussion.

OBJECTIVE: To evaluate the impact of an evidence-based assessment program for people with workers' compensation claims for concussion on healthcare utilization and duration of lost time from work. SETTING: An assessment program for people with a work-related concussion was introduced to provide physician assessment focused on education and appropriate triage. PARTICIPANTS: A total of 3865 people with accepted workers' compensation claims for concussion with dates of injury between January 1, 2014, and February 28, 2017. DESIGN: A quasiexperimental pre-/poststudy of healthcare utilization (measured by healthcare costs) and duration of time off work (measured by loss of earnings benefits) in a cohort of people with workers' compensation claims for concussion in the period prior to and following introduction of a new assessment program. Administrative data were retrospectively analyzed to compare outcomes in patients from the preassessment program implementation period to those in the postimplementation period. RESULTS: The assessment program resulted in reduced healthcare utilization reflected by a 14.4% (95% confidence interval, -28.7% to -0.8%) decrease in healthcare costs. The greatest decrease in healthcare costs was for assessment services (-27.9%) followed by diagnostic services (-25.7%). There was no significant difference in time off work as measured by loss-of-earnings benefits. CONCLUSION: A care model for people with a work-related concussion involving an evidence-based assessment by a single physician focused on patient education resulted in significantly decreased healthcare utilization without increasing duration of time off work.

Using existing case-mix methods to fund trauma cases.

Policymakers frequently face the need to increase funding in isolated and frequently heterogeneous (clinically and in terms of resource consumption) patient subpopulations. This article presents a methodologic solution for testing the appropriateness of using existing grouping and weighting methodologies for funding subsets of patients in the scenario where a case-mix approach is preferable to a flat-rate based payment system. Using as an example the subpopulation of trauma cases of Ontario lead trauma hospitals, the statistical techniques of linear and nonlinear regression models, regression trees, and spline models were applied to examine the fit of the existing case-mix groups and reference weights for the trauma cases. The analyses demonstrated that for funding Ontario trauma cases, the existing case-mix systems can form the basis for rational and equitable hospital funding, decreasing the need to develop a different grouper for this subset of patients. This study confirmed that Injury Severity Score is a poor predictor of costs for trauma patients. Although our analysis used the Canadian case-mix classification system and cost weights, the demonstrated concept of using existing case-mix systems to develop funding rates for specific subsets of patient populations may be applicable internationally.

The effect of rostering with a patient enrolment model on emergency department utilization.

OBJECTIVE: To assess the effect of rostering with a patient enrolment model (PEM) in Ontario on emergency department utilization for non-emergent care. DATA SOURCES/STUDY SETTING: Administrative data for fiscal years 2006/07 through 2010/11 from the Ontario Ministry of Health and Long-Term Care were used for the analysis. STUDY DESIGN: Patient-level analysis with a difference-in-difference modelling approach was used to study the relationship. A control group was established using propensity score matching. PRINCIPAL FINDING: Results suggest that rostering with a PEM is associated with a statistically significant reduction in emergency department (ED) (non-emergent) visits in Ontario. More specifically, enrolment with PEMs reduced ED visits by 3% during the study period, translating into cost savings of approximately $8 million for hospitals in Ontario. CONCLUSION: This study shows that PEMs have achieved some degree of success in enhancing health system efficiency in Ontario through the reduction in the use of EDs for non-emergent care.

Predicting patients with high risk of becoming high-cost healthcare users in Ontario (Canada).

Literature and original analysis of healthcare costs have shown that a small proportion of patients consume the majority of healthcare resources. A proactive approach is to target interventions towards those patients who are at risk of becoming high-cost users (HCUs). This approach requires identifying high-risk patients accurately before substantial avoidable costs have been incurred and health status has deteriorated further. We developed a predictive model to identify patients at risk of becoming HCUs in Ontario. HCUs were defined as the top 5% of patients incurring the highest costs. Information was collected on various demographic and utilization characteristics. The modelling technique used was logistic regression. If the top 5% of patients at risk of becoming HCUs are followed, the sensitivity is 42.2% and specificity is 97%. Alternatives for implementation of the model include collaboration between different levels of healthcare services for personalized healthcare interventions and interventions addressing needs of patient cohorts with high-cost conditions.

High-cost users of Ontario's healthcare services.

Approximately 1.5% of ontario's population, represented by the top 5% highest cost-incurring users of ontario's hospital and home care services, account for 61% of hospital and home care costs. Similar studies from other jurisdictions also show that a relatively small number of people use a high proportion of health system resources. Understanding these high-cost users (hcus) can inform local healthcare planners in their efforts to improve the quality of care and reduce burden on patients and the healthcare system. To facilitate this understanding, we created a profile of hcus using demographic and clinical characteristics. The profile provides detailed information on hcus by care type, geography, age, sex and top clinical conditions.

Tuberculosis-HIV co-infection in Kiev City, Ukraine.

In 2004, we tested all patients with newly diagnosed tuberculosis (TB) for HIV in Kiev City. The results were compared to information from medical records of 2002, when co-infection prevalence was 6.3%. Of 968 TB patients, 98 (10.1%) were HIV infected. TB-HIV co-infection is increasing, especially in injecting drug users.