Optimizing healthcare processes — bottom-up or top-down?
Healthcare processes are complex networks that need to simultaneously balance different demands and supplies subject to limited resources and organizational requirements and restrictions. This article is a first exploration into bottom-up and top-down approaches to search for this balance.
Denmark has been investing heavily in its healthcare infrastructure, with an ongoing plan to build, by 2025, six new hospitals in Aarhus, Aalborg, Gødstrup, Odense, Køge, and Hillerød and expand and update existing hospitals, with planned construction costs of about 42.7 billion kroner, or about US$ 7 billion (1)(2).
Some of the expectations are that by 2025 the number of ambulatory treatments, that is, patients that will not need a hospital bed, will increase by 50% and the number of beds needed will decrease by 20%. Denmark has set out to invest in e-Health technologies that connect physicians with one another and with pharmacies, as well as telemedicine that allows for diagnosis and treatment at home, changing the nature of the traditional care pathway.
The care pathway is the sequential representation of all the different tasks performed by any healthcare professional and the processes involved in patient care.
The Danish government decided that it will not cover overbudgets in these projects, and any difference will have to be covered by performance improvements in the hospitals themselves (1). As a result, Denmark is under more pressure than ever to improve healthcare processes.
Optimization of healthcare processes has to do with finding ways to balance the demand for a service or product with the availability of that service or product, meeting all restrictions and requirements from the organization while using as few resources as possible. It can be a very difficult process to achieve this balance in healthcare organisations with many resources, limitations, and requirements. Two extreme approaches to doing this are either starting from the people actually doing the actions (bottom up) or from a high-up planning perspective (top down). What are these approaches? Let us explore them briefly.
Bottom up process optimization
A bottom-up optimization describes any process that develops from a culture of continuous improvement, where everybody is permanently trying to improve the processes they are involved in. These methods concentrate on 1) increasing the value added by activities, 2) reducing as much as possible non-value-adding activities, 3) reducing the variability in activities, and 4) reducing the complexity of the activities. Bottom-up processes are therefore mainly operational.
The focus of these methods is fundamental for teamwork, as it requires a group of healthcare professionals to agree on what "value" actually is and to regularly evaluate (and, if necessary, modify) their activities in terms of their contribution to this value.
Established processes that take a bottom-up approach include, for example, lean, six-sigma, the theory of constraints (TOC), total quality management (TQM), and business process redesign (BPR).
Specific implementation examples of bottom-up approaches to continuous improvement applicable to healthcare are white boards in hospital corridors where nurses make remarks about the problems they encounter during the day, and once a week these problems are discussed within a group and actions are decided to address them.
Top-down optimization
A top-down optimization describes any process that designs and optimises the integral planning and control of processes, considering the complete care pathway, all managerial areas, and all levels of control (strategic, tactical, and operational).
Integrated optimization is very important because optimization is one managerial area in the care pathway that may harm the overall performance of the healthcare system. Also, cross-hierarchical optimization is relevant because conditions are different depending on the hierarchical level of the planning you are making.
Conditions are different depending on the hierarchical level of the optimization that you are making.
For example, if the optimization of the use of beds in a hospital is done at the operational level, the demand is generally known as the number of elective treatments to be carried out and the number of beds needed. At the same time, the supply is inflexible as specialised hospital beds need time to be bought and the nurse schedule cannot be changed without some cost. Possible actions are the reassignment of nurse tasks or the reassignment of existing beds, for example.
On the other extreme, if the optimization is done at the strategic level, the demand will be a forecast based on past information, yet the supply is flexible, with the possibility of projecting the purchase of the necessary beds and the scheduling of the nurses required. Optimization models using agent-based modelling (ABM), discrete event modelling (DEM), or system dynamics (SD) can be used.
An underexplored level is the tactical level, where the demand is partially known and the supply may be partially flexible, which presents an opportunity to venture approaches that manage the variability and partial information, either through the use of numerical optimization models using probability distributions to approximate these unknowns or by building possibilities for reactions based on the potential need and activating some of these actions depending on the actual need when it becomes clear, through methods like real options.
So, which one is better?
It depends on the case, but it is unlikely an organisation will be able to thrive without allowing for both approaches to co-exist, involving managers, setting the boundaries and preparing for expected variability through strategy, and involving operators, thriving within the boundaries and reacting to daily variability through operations.
(1) Andersen MM, Andersen U, 2021. “Budgetmodel pånye sygehuse vil presse behandlingen”. Ingeniøren (20), 21 Maj 2021, pp.8–9 (2) https://godtsygehusbyggeri.dk/byggeprojekter
