Based on the research of Sarang Deo, Seyed Iravani, Tingting Jiang, Karen Smilowitz and Stephen Samuelson
Sarang Deo, Associate Professor of Operations Management at ISB,and colleagues design a ‘mobile care delivery model’ that enables chronic care mobile clinics to determine optimum capacity allocation, and provision of optimal health services to patients, based on their medical condition. The model allows the quantification and comparison of patients’ future health states in order to aid health providers in making optimal decisions related to patient scheduling. The use of data from Mobile C.A.R.E Foundation, a community-based provider of pediatric asthma care in Chicago, and extensive computational experiments suggest that the mobile care delivery model can improve health gains of a community by 15%.
Recent times have witnessed an increase in community-based health programs aimed at maximizing health services to underserved populations. Prominent among the several health care interventions are mobile clinics (aka clinic-on-wheels) that are specially kitted vans with medical equipment and diagnostic facilities that aid general examination, consultation, and prescription to outpatients by a general practitioner or a specialist doctor in a mobile vehicle.
For a country like India, where a large part of the population is underserved and cut off from traditional healthcare services, mobile clinics attempt to bridge the gap. As of December 2014, there were about 1301 operational mobile clinics in 368 districts across India (Source: The Ministry of Health and Family Welfare, Government of India). These mobile clinics move to different communities and provide health promotion services, preventive education and curative services across the country.
Several non-profit organizations and private players have been actively involved in providing rural health care using mobile vans. For instance, in Andhra Pradesh, the Health Management and Research Institute (HMRI), a non-profit organization, uses 100 mobile vans to take health services to the doorsteps of the rural poor residing beyond three kilometers from the primary health centre (Source: The Hindu Business Line, February 22, 2008). In Sangrur district of Punjab, a private organization called Mobital runs mobile vans to help women, children and the elderly with healthcare. Patients of Mobital are charged affordable fees that is slightly higher than the charges levied at government facilities and significantly lower than the rates charged by private healthcare clinics in cities (Source: http://www.mobital.in).
The Challenge of Chronic Diseases
Mobile vans are primarily used to provide episodic care that mostly involves preventive check-ups, health promotion and diagnostics. They are not designed to provide ongoing medical care and long-term monitoring. Yet, changing disease profiles and the growing threat of chronic conditions require continuous monitoring and long-term treatment. According to the Public Health Foundation of India, New Delhi, chronic diseases are the leading cause of deaths in India accounting for sixty percent of all deaths annually, killing more than five million people every year (Source: http://southasia.oneworld.net/news). The World Health Organization suggests the adoption of ‘public health surveillance’ in the provision of chronic care. It recommends continuous, systematic collection, analysis and interpretation of health-related data needed for the planning, implementation, and evaluation of public health practices in case of chronic diseases.
Issues in Implementing Mobile Chronic Care
Mobile vans catering to chronic diseases automatically inherit all the challenges related to resource shortages and capacity constraints that are faced by conventional mobile vans. Thus, the problem of limited appointment slots will continue to linger. In addition, chronic diseases involve disease progression, i.e., different patients have varying degrees of the illness at any given time. With limited resources, chronic care mobile clinics are faced with the additional challenge of prioritizing which patients to see based on their transient health conditions caused by the chronic disease and the prior medical visit. The biggest challenge, therefore, is to synchronize the visit frequency of the mobile clinic to each community with the visit frequency of patients treated in those communities. That is, the ideal appointment dates for patients in a given community must match the mobile clinic’s visit dates to that community. Let’s say a patient’s next appointment at a mobile clinic should ideally be after three weeks. Then, this patient can avail the mobile clinic facility only if the mobile clinic visits the patient’s community after three weeks. What if the mobile clinic does not go to the patient’s community for the next five weeks? Such a challenge is unique to mobile chronic care and does not arise in stationary clinics that are always there for patients to come in at any time.
According to Sarang Deo, Associate Professor of Operations Management at the Indian School of Business, “Chronic care mobile clinics can boost chronic health care provision, especially in remote and rural areas, by repeated use of mobile vans. However, with chronic care mobile vans having limited slots and frequency of visits to a certain community, only a restricted number of patients can be seen during a given visit. Merely increasing utilization of mobile vans and filling up all appointment slots need not yield optimal health outcomes for the community. You might end up seeing some patients much more often (with little incremental improvement in their health) and not see other patients often enough (thereby deteriorating their health).”
Thus, several questions arise: Which patients to see? How to decide which patients will need more care during the next visit? And how to prioritize sick patients and allocate limited appointment slots to get optimal benefits in non-profit settings?
A Delivery Model for Mobile Care
Deo and his colleagues found answers in an unlikely place – Inner city, Chicago. They studied data from Mobile C.A.R.E Foundation (MCF), a school-based asthma care provider for children in Chicago,and designed a ‘mobile care delivery model’ to help chronic care mobile units overcome capacity allocation challenges.
MCF has been operating for more than ten years as a non-profit organization dedicated to providing free, comprehensive asthma treatment and health education to children in Chicago’s underserved communities via mobile medical clinics called the “asthma vans”. It has served more than 6,000 vulnerable students through 33,000 patient visits. MCF mobile clinics are different from conventional mobile clinics as they provide staffed asthma specialists to ensure that children receive consistent care from the same healthcare provider.
Using the rooster of active schools and by scheduling visits to these schools, the MCF staff determine the allocation capacity at each school through daily patient schedules. The schedules are based on the medically recommended treatment durations for the patients that are modified based on the available capacity at each school.
However, this current practice of specifying fixed duration between visits for patients of different health states is not much good in case of a chronic disease like asthma that requires ongoing care and monitoring, and customized services for patients. According to Deo, to achieve optimal health outcomes, “The right operational metric to look at is – what is the revisit interval i.e. time between two successive visits?”
Deo and colleagues’ mathematical model takes past trajectory of patients’ health conditions to estimate the health condition of each patient, and to determine which patients will require an appointment during the next visit. By projecting the health condition of each patient, the model allows prioritization of “more sick” patients to ensure that limited resources are allocated in the best possible manner, leading to better health outcomes for the entire patient population.
This proposed model allows for efficient calculation of the health condition and disease progression of every student in the near future, thereby allowing prioritization of patients with worse health conditions. Specifically, it facilitates flexibility in adjusting visit frequencies to accommodate limited capacity and to prioritize patients in worse health states. Overall, extensive computational experiments indicate near optimal performance while improving health gains of the community by 15%.
Lessons for Healthcare Providers in India
Application of the model shows significant improvement in health outcomes by flexibly adjusting the visit duration to accommodate capacity constraints and health states of the entire patient population. Moreover, this flexibility reduces the number of patients never seen, thus improving access. Thus, it enables chronic care mobile clinics to determine optimum capacity allocation and provision of optimal health services to patients based on their medical condition.
Although the research is based on data from Chicago, the results indicate that there are clear benefits in adopting the model for mobile chronic care in India too. Indian healthcare providers too stand to gain significant improvements in health care outcomes through better capacity allocation decisions in community-based chronic care settings.
About the Researchers:
Sarang Deo is Associate Professor of Operations Management at the Indian School of Business, Hyderabad.
Seyed M.R. Iravani is Professor of Industrial Engineering and Management Sciences at the McCormick School of Engineering, Northwestern University.
Tingting Jiang was a PhD scholar at the Department of Industrial Engineering and Management Sciences, Northwestern University and is now at Deutsche Bank.
Karen Smilowitz is Charles Deering McCormick Professor of Teaching Excellence and Professor of Industrial Engineering and Management Sciences at the McCormick School of Engineering, Northwestern University.
Stephen Samuelson was Executive Director of Mobile CARE Foundation at the time of conducting this research and is now President & CEO of the Frisbie Senior Center, Des Plaines, Illinois.
About the Research:
Sarang Deo, Seyed Iravani, Tingting Jiang, Karen Smilowitz, Stephen Samuelson (2013) Improving Health Outcomes Through Better Capacity Allocation in a Community-Based Chronic Care Model. Operations Research 61(6):1277-1294.
This research was funded by the National Science Foundation, USA. The title of the grant is “Design and Control Principles for Mobile Health Care Operations Management – The Case of Asthma Control, NSF CMII-1131298.”
About the Writer:
Catherine Xavier is a writer with the Centre for Learning and Management Practice at the Indian School of Business.