Fundamental Principles of Efficient Healthcare Operations Management. 

In the current era of great demand for much more efficient use of available resources and cost reduction, many healthcare organizations, faced with uncertain demand and reduced reimbursement, struggle with typical issues such as:

• Capacity: How many beds, operating rooms or pieces of equipment are needed for different services?
• Staffing: How many nurses and other providers are needed for a particular shift in a unit?
• Scheduling: How to optimally schedule the required staff for particular shifts?
• Patient Flow: What maximal patient delays at the various points of care are acceptable to achieve the system throughput goals?

• Resource Allocation: What minimal resources (material, human, financial) are required for different patient service lines?
• Forecasting: How to estimate the future patient demand or transaction volumes?
• Comparing Productivity of Units with Multiple Inputs and Outputs: How to combine different productivity metrics into one total score for each unit?
• Optimizing a Supply Chain and Inventory Management: How to manage the supply chain to minimize the total procurement costs?

This list can easily be extended to include many other operational issues. Why is it so challenging for many organizations to develop and implement efficient and sustainable long-term solutions?

First, most managerial decisions are made in a highly variable and random environment. It is a general human tendency to avoid the complications of incorporating uncertainty into decision-making by ignoring it or turning it into certainty. For example, average procedure times or average queue sizes are typically treated as if they are fixed values, ignoring the effect of non-symmetrical (non-normal) variability around these averages. Such a practice usually results in significantly inaccurate conclusions. Another factor is that complex systems usually contain multiple internal interdependencies of units and staff. Traditional management based on experience, intuition, and simple linear projections lacks a means of capturing such interdependencies and expecting the response of one unit to the change in other units. But such foresight is critical for making sustainable and justified managerial decisions, both operational and strategic. This is a root cause of the frequently observed unintended consequences of managerial decisions that look reasonable on the surface.

Truly justified and efficient managerial decisions can be achieved only using data analytics and simulation modeling methodology, which is a foundation of management science. Management science is a systematic way of developing managerial decisions for allocating material, human, and financial resources that best meet an organization’s performance objectives. Decisions for leveraging resources that best meet system performance objectives should be based on comparative analysis of validated mathematical and computer simulation models. Ignoring this approach can lead to serious management miscalculations.

At the same time, the decision-makers do not need to know technical details of data analytics and simulation modeling. The latter is another profession and a separate area of expertise.  However, decision-makers do need to know and practice the fundamental scientific management principles. These principles are defined as the widely-tested insights that are both highly general (applicable in many settings), and stable (applicable now and in the future). At the same time, much in management cannot yet be captured as fundamental principles, allowing some flexibility and judgement based on local conditions and the organization’s culture. Thus, management is both science and art. Nonetheless, understanding the fundamental management principles is highly valuable as a starting point for efficient management of operations.

Some general management principles related to capacity, patient flow, and staffing are provided below. Detailed explanations and illustrations of these principles are provided in the book “Healthcare Management Engineering in Action”, Springer Nature, 2024 http://www.springer.com/us/book/9781461420675

 Some principles of capacity, patient flow, and staffing management

•  For systems with a similar type of service, mutually interchangeable (pooled) resources are more efficient in terms of patient wait time and throughput than specialized (dedicated) resources with the same total ratio capacity/workload.
• Specialized dedicated resources (staff, operating or procedure rooms, beds, etc) typically cost more than mutually interchangeable (pooled) resources.
• Because of the variability of patient arrivals and service time, a reserved buffer capacity is usually needed to avoid regular operational problems due to excessive wait time and long lines.
• Size matters. Large hospitals (units) always have better operational performance characteristics (lower wait time and the number of patients in the queue, higher utilization) than small hospitals (units) with the same patient volume to size ratio.
• Generally, the higher utilization level of the resource (good for the organization), the longer the wait times to get that resource (bad for patients).
• Improvement of separate subsystems or units/departments (local optimization or local improvement) does not usually result in the improvement of the entire system. A proper system improvement methodology must take into account the subsystem’s interdependency.
• Scheduling appointments (jobs) in the order of their increased duration variability (from lower to higher variability) results in a lower overall cycle time and patient wait time.
• In a series of dependent events, only a bottleneck defines the throughput of the entire system. A bottleneck is a resource (or activity) whose capacity is less than or equal to the demand placed on it.
• Capacity, staffing, and financial estimation based on average input values without taking into account the variability around the averages results in misappropriation of required resources (except for a strictly linear relationship between the input and output). This is called the flaw (deception) of averages.
• In a single time period with random demand, the capacity (staffing level) that minimizes the total cost of overage and underage is defined by the ratio of underage to the sum of under- and overage equated to the cumulative probability distribution of demand (Newsvendor framework).

 Other important groups of fundamental principles, such as information and human behavior principles, are provided by Hopp & Lovejoy in the excellent reference “Hospital Operations: Principles of High Efficiency Health Care”.

 The Bottom line.

The fundamental principles of management are as objective as the laws of physics in natural sciences.  However, laws of physics cannot be violated, while the laws of management can and are violated frequently. Organizations pay a heavy price for doing so. At the same time, knowledge of the fundamental management principles helps the decision-makers to steer in the right direction without plunging into technical details of data analytics and simulation modeling that form the basis and justification for these principles.