Realist analysis of streaming interventions in emergency departments

BackgroundSeveral of the many emergency department (ED) interventions intended to address the complex problem of (over)crowding are based on the principle of streaming: directing different groups of patients to different processes of care. Although the theoretical basis of streaming is robust, evidence on the effectiveness of these interventions remains inconclusive.MethodsThis qualitative research, grounded in the population-capacity-process model, sought to determine how, why and under what conditions streaming interventions may be effective. Data came from a broader study exploring patient flow strategies across Western Canada through in-depth interviews with managers at all levels. We undertook realist analysis of interview data from the 98 participants who discussed relevant interventions (fast-track/minor treatment areas, rapid assessment zones, diverse short-stay units), focusing on their explanations of initiatives’ perceived outcomes.ResultsEssential features of streaming interventions included separation of designated populations (population), provision of dedicated space and resources (capacity) and rapid cycle time (process). These features supported key mechanisms of impact: patients wait only for services they need; patient variability is reduced; lag time between steps is eliminated; and provider attitude change promotes prompt discharge. Conversely, reported failures usually involved neglect of one of these dimensions during intervention design and/or implementation. Participants also identified important contextual barriers to success, notably lack of outflow sites and demand outstripping capacity. Nonetheless, failure was more commonly attributed to intervention flaws than to context factors.ConclusionsWhile streaming interventions have the potential to reduce crowding, a theory-based intervention relies on its implementers’ adherence to the theory. Streaming interventions cannot be expected to yield the desired results if operationalised in a manner incongruent with the theory on which they are supposedly based.

Perception of Social Health Insurance Program among Community People in Pokhara, Nepal

Perception of social health insurance program is the way in which it is understood or interpreted in terms of different factors; quality of care service delivery adequacy, benefits of program, convenience, price, providers attitude, peer pressure, Community beliefs and attitudes. The main aim of this study is to identify community people’s perception and the factors influencing the perception of people towards social health insurance program. Data from total of 105household registered in insurance program (insured) were collected. Descriptive cross sectional study design was used. The data were analyzed and interpreted by using descriptive and inferential statistics through the computer program SPSS 16 version and presented in tables. Findings revealed that age group range from 20 to 77 years, 72.4 percent family size 5 or below 74.3 percent utilized the insurance services from the accredited health facility. Regarding the reason for enrollment, 83.8 percent said financial protection against illness, 50.8 percent gave the reason that they had to buy drugs outside facility as the main reason for not renewing the program in future. Price of program related response was top perceived factor with mean 3.97±0.70 whereas provider attitude was low perceived factor with mean 1.95± 0.58. The study concluded that the price of program related factors were the top perceived factors whereas provider attitude related factors were low perceived factors that may influence for the perception of community people towards social health insurance program.

Tradeoff between efficiency and perceived quality: evidence from patient-level data

Objectives To estimate technical efficiency scores of District Headquarter Hospitals (DHQHs) for obstetric services and to explore the relationship between the efficiency of DHQHs and the patients’ satisfaction about the quality of services provided. Design, Setting and Participants Data from Health Facility Assessment (HFA) survey is used for efficiency measurement. The data on patient’s perceptions and other control variables are taken from Client Exit Interviews part of the HFA survey. Two-stage residual inclusion, Ordered Logistic Regression and Least square dummy variable techniques are used to investigate the relationship between technical efficiency and patients’ satisfaction level. Main Outcome Measure(s) and Results The average efficiency score for Pakistan’s DHQHs is 0.52, and not a single hospital is fully efficient. Moreover, the relationship between technical efficiency and patients’ satisfaction is found to be negative and statistically significant indicating that an increase in hospital efficiency tends to decrease patients’ satisfaction. The disaggregated analysis reveals that patients’ satisfaction associated with the healthcare provider attitude and communication is more affected by technical efficiency. Conclusion Patients’ satisfaction level is more sensitive to physician’s attitude and communication. This makes sense because the longer the consultation time, the more accurate the diagnosis would be. This, together with a comforting and confident physician, is likely to achieve better patients’ satisfaction.