Managerial Intervention Strategies to Reduce Patient No-Show Rates

High patient no-show rates increase health care costs, decrease healthcare access, and reduce the clinical efficiency and productivity of health care facilities. The purpose of this exploratory qualitative single case study was to explore and analyze the managerial intervention strategies healthcare administrators use to reduce patient no-show rates. The targeted research population was active American College of Healthcare Executives (ACHE), Hawaii-Pacific Chapter healthcare administrative members with operational and supervisory experience addressing administrative patient no-show interventions. The conceptual framework was the theory of planned behavior. Semistructured interviews were conducted with 4 healthcare administrators, and appointment cancellation policy documents were reviewed. Interpretations of the data were subjected to member checking to ensure the trustworthiness of the findings. Based on the methodological triangulation of the data collected, 5 common themes emerged after the data analysis: reform appointment cancellation policies, use text message appointment reminders, improve patient accessibility, fill patient no-show slots immediately, and create organizational and administrative efficiencies. Sharing the findings of this study may help healthcare administrators to improve patient health care accessibility, organizational performance and the social well-being of their communities

Guidelines for Scheduling in Primary Care: An Empirically Driven Mathematical Programming Approach

Primary care practices play a vital role in healthcare delivery since they are the first point of contact for most patients, and provide health prevention, counseling, education, diagnosis and treatment. Practices, however, face a complex appointment scheduling problem because of the variety of patient conditions, the mix of appointment types, the uncertain service times with providers and non-provider staff (nurses/medical assistants), and no-show rates which all compound into a highly variable and unpredictable flow of patients. The end result is an imbalance between provider idle time and patient waiting time. To understand the realities of the scheduling problem we analyze empirical data collected from a family medicine practice in Massachusetts. We study the complete chronology of patient flow on nine different workdays and identify the main patient types and sources of inefficiency. Our findings include an easy-to-identify patient classification, and the need to focus on the effective coordination between nurse and provider steps. We incorporate these findings in an empirically driven stochastic integer programming model that optimizes appointment times and patient sequences given three well-differentiated appointment types. The model considers a session of consecutive appointments for a single-provider primary care practice where one nurse and one provider see the patients. We then extend the integer programming model to account for multiple resources, two nurses and two providers, since we have observed that such team primary care practices are common in the course of our data collection study. In these practices, nurses prepare patients for the providers’ appointments as a team, while providers are dedicated to their own patients to ensure continuity of care. Our analysis focuses on finding the value of nurse flexibility and understanding the interaction between the schedules of the two providers. The team practice leads us to a challenging and novel multi step multi-resource mixed integer stochastic scheduling formulation, as well as methods to tackle the ensuing computational challenge. We also develop an Excel scheduling tool for both single provider and team practices to explore the performance of different schedules in real time. Overall, the main objective of the dissertation is to provide easy-to-implement scheduling guidelines for primary care practices using both an empirically driven stochastic optimization model and a simulation tool

Single visit model in Finnish municipal dental care: A more efficient service model for low-complexity patients

For several years, the public dental care system of Finland has been facing difficulties concerning long waiting times and resource sufficiency. The demand for public dental services is likely to increase in coming years due to ageing population. The high demand and scarce resources of public dental services are building pressure on adopting new service models to ensure the availability and effectiveness of Finland's public dental services in the future. Previous studies in the field of healthcare have shown that service delivery efficiency can be improved by designing the service production to match the actual needs of the patients. However, the highly variant needs of public sector patients make tailoring the service delivery challenging. By recognizing more homogeneous patient groups, the service delivery could be better designed to match supply and demand. For example, Lean techniques such as flexible, just-in-time (JIT) scheduling and open-ended appointments could be utilized to reduce slack and to improve the productivity of staff. This type of approach has become more common, especially in emergency clinics, aspiring to improve the flow of low-complexity patients. However, in dentistry, Lean thinking is still in its infancy. This thesis poses a setting where the Finnish public dental care could assume two different operating modes: the traditional mode currently used by municipalities and a single visit (SV) mode. The SV mode would act as a fast-track for low-complexity patients who only require a reduced set of basic procedures by providing all necessary treatment during a single visit. To understand both of the modes, the operations of two municipalities - Jyväskylä and Espoo - and a private SV clinic - Megaklinikka - were analyzed. By mining data on staff, visits and performed procedures, the differences between the two modes in terms of operating model, patient & case mix and operational efficiency were examined. The operation of an additional SV service line in one dental care unit of Jyväskylä was also simulated. Unlike the traditional model, the SV model allows dentists and hygienists to switch rooms and utilizes open-ended appointments and an ERP system to synchronize a JIT-flow of patients. Due to these features and a more homogenous patient and case mix, the SV model is able to produce ~90% more procedures and treat ~68% more patients annually than the traditional model in relation to the amount of clinical staff. Per one dentist, the SV model requires 20% less nurses and 120% more hygienists than the traditional model. The SV model results to 44% less visits, as 80% more procedures can be performed during a single visit. Roughly 40% of all patients and 70% of adult patients in municipalities could be classified as basic patients, meaning that for the majority of adult patients, the SV model could be applied. The simulation suggested that a SV service line would increase the annual procedure output of a municipal dental care unit by 7% without any additional staff. To harness this approach on a larger scale, the proportion of hygienists should be roughly doubled in municipalities. The results of this thesis show that the SV service model could offer a way to treat the majority of adult patients more efficiently in Finnish municipal dental care

Exploring business models to provide a foundation for enhanced eye care services in high street optometric practice

High street optometric practices are for-profit businesses. They mostly provide sight testing and eye examination services and sell optical products, such as spectacles and contact lenses. The sight testing services are often sold at a vastly reduced price and profits are generated primarily through high margin spectacle sales, in a loss leading strategy. Published literature highlights weaknesses in this strategy as it forms a barrier to widening the scope of services provided within optometric practices. This includes specialist non-refraction based services, such as shared care. In addition this business strategy discourages investment in advanced diagnostic equipment and higher professional qualifications. The aim of this thesis was to develop a greater understanding of the traditional loss-leading strategy. The thesis also aimed to assess the plausibility of alternative business models to support the development of specialist non-refraction services within high street optometric practice. This research was based on a single independent optometric practice that specialises in advanced retinal imaging and offers a broad range of shared care services. Specialist non-refraction based services were found to be poor generators of spectacle sales likely due to patient needs and presenting concerns. Alternative business strategies to support these services included charging more realistic professional fees via cost-based pricing and monthly payment plans. These strategies enabled specialist services to be more self-sustainable with less reliance on cross-subsidy from spectacle sales. Furthermore, improving operational efficiency can increase stand-alone profits for specialist services.Practice managers may be reluctant to increase professional fees due to market pressures and confidence. However, this thesis found that patients were accepting of increased professional fees. Practice managers can implement alternative business models to enhance eye care provision in high street optometric practices. These alternative business models also improve revenues and profits generated via clinical services and improve patient loyalty

Gestión óptima de citas médicas mediante la aplicación de un modelo de optimización multicriterio

The Cuban health system is known worldwide for its good results in the prevention of diseases and for its continuous improvements in health standards. However, we want to go a step further and put the patient at the center of the system. To this task, we propose a methodology that allows a complete automatization of the medical appointment system so that patients will know in advance the average time they will spend in the hospital the day of the appointment, as well as the sequence of medical tests that will have to undertake. We develop a non linear integer multi-goal model (NLIMGM) model to perform an optimal distribution of patients. Our model assign appointments to patients according to two different goals: to give the appointments as soon as possible and to minimize the time that a patient would spend in the hospital to complete a protocol.The performance of the resulting NLIMGM policies was compared with traditional practices decision rules for the diagnosis and treatment of ophthalmology diseases in a Cuba hospital. The results show that this method outperforms traditional methods by far. Specifically, this approach will increase the efficiency of appointments scheduling and reduces the total diagnosis time for cataract, cornea, glaucoma by 46% on average or, in other words, by roughly one hour, relative to the standard approach. Arguably, this translates into improved patient satisfaction and efficiency in the use of resources in health services. <br /