[Value-based healthcare in mental health care: a practical example]. / Waardegedreven zorg in de ggz: een praktijkvoorbeeld.

BACKGROUND: To promote quality improvement in Dutch mental health care, it is imperative to line up methods of quality improvement with daily care practice. In value-based healthcare the joint vision of medical professionals and patients is leading in the design and execution of care improvements. AIM: To demonstrate the value of value-based care. METHOD: Description of a practical example of the application of value based healthcare in mental healthcare, including a practical example of value-based improvement of patient care. RESULTS: A multidisciplinary improvement team was formed, consisting of patients and employees who were involved in the depression care pathway. The team determined important outcomes of care from the patient’s perspective, and successfully executed several improvement initiatives. CONCLUSION: The bottom-up approach of value-based healthcare is in line with day-to-day care practice and provides opportunities for effectively improving the quality of mental health care.

Agent-Based Models for Assessing Complex Statistical Models: An Example Evaluating Selection and Social Influence Estimates from SIENA.

Although agent-based models (ABMs) have been increasingly accepted in social sciences as a valid tool to formalize theory, propose mechanisms able to recreate regularities, and guide empirical research, we are not aware of any research using ABMs to assess the robustness of our statistical methods. We argue that ABMs can be extremely helpful to assess models when the phenomena under study are complex. As an example, we create an ABM to evaluate the estimation of selection and influence effects by SIENA, a stochastic actor-oriented model proposed by Tom A. B. Snijders and colleagues. It is a prominent network analysis method that has gained popularity during the last 10 years and been applied to estimate selection and influence for a broad range of behaviors and traits such as substance use, delinquency, violence, health, and educational attainment. However, we know little about the conditions for which this method is reliable or the particular biases it might have. The results from our analysis show that selection and influence are estimated by SIENA asymmetrically and that, with very simple assumptions, we can generate data where selection estimates are highly sensitive to misspecification, suggesting caution when interpreting SIENA analyses.

As the twig is bent.

The trauma of having no home imposes unique health problems on families, especially children. Homeless children are more subject to infectious diseases, are more in need of immunizations, and have more growth and developmental problems than children growing up in nurturing environments. A study of the homeless in St. Louis found that increasingly they are families headed by single women who had to leave their homes mainly because of overcrowding. A study of data obtained from healthcare clinics set up at shelters showed that the mean age of homeless children was 4.9 years; the mean number of months they had lacked a permanent residence was more than four. Healthcare screenings showed that chronic health problems, such as asthma and anemia and deficits in growth and development, were among the top 10 diagnoses. Less serious problems like colds and lice were more prevalent, however. Intelligence tests administered to homeless children showed that they had three times the cognitive and developmental problems that would be expected among children in general.

Monitoring medication compliance: description of a new device.

Patients' noncompliance with medication regimens is a common explanation for the failure of outpatient therapy. Advances in understanding medication noncompliance and approaches to dealing with the problem have been considerably hindered because no satisfactory technique for measuring medication compliance exists. This report describes a new method for electronically monitoring compliance involving tablet medications through the use of a device that automatically records the date and time medication is removed. Data are rapidly recoverable from the monitor and can be presented in two easily interpreted formats. Simple to use, reliable, accurate, relatively small, and inexpensive, the monitor should be useful in clinical drug trials, compliance research, and clinical medicine.