Case mix classification and a benchmark set for surgery scheduling

Abstract

Numerous benchmark sets exist for combinatorial optimization problems. However, in healthcare scheduling, only a few benchmark sets are known, mainly focused on nurse rostering. One of the most studied topics in the healthcare scheduling literature is surgery scheduling, for which there is no widely used benchmark set. An effective benchmark set should be diverse, reflect the real world, contain large instances, and be extendable. This paper proposes a benchmark set for surgery scheduling algorithms, which satisfies these four criteria. Surgery scheduling instances are characterized by an underlying case mix, which describes the volume and properties of the surgery types. Given a case mix, unlimited random instances can be generated. A complete surgery scheduling benchmark set should encompass the diversity of prevalent case mixes. We therefore propose a case mix classification scheme, which we use to typify both real-life and theoretical case mixes that span the breadth of possible case mix types. Our full benchmark set contains 20,880 instances, with a small benchmark subset of 146 instances. The instances are generated based on real-life case mixes (11 surgical specialties), as well as theoretical instances. The instances were generated using a novel instance generation procedure, which introduces the concept of "instance proximity" to measure the similarity between two instances, and which uses this concept to generate sets of instances that are as diverse as possible.

Managing urgent care in hospitals

“…As the case mix may effect performance, we perform an extensive scenario analysis, including different case mixes. To create distinct case mixes, we use the classification put forward by Leeftink and Hans [162]. They classify surgery types using two parameters: surgery duration (relative to total OR capacity) and coefficient of variation (CV) of the surgery duration, which both indicate the level of complexity involved in scheduling surgeries.…”

“…The surgery types underlying the dataset used to construct scenarios and evaluate policies is the same as used by Leeftink and Hans [162]. This dataset contains surgery types, defined as 3-parameter lognormal distributions, which are shown to fit well to surgery duration distributions [178,222], and denotes the (relative) frequency with which these surgeries take place.…”

Organizing timely treatment in multi-disciplinary care

“…To increase the practical applicability, a comparison of the investigated approach with the current hospital practice can be made, using historical data from a hospital. To increase generality in a wide range of healthcare institutes, not only the specific parameter settings of one hospital, but also a wide range of other possible parameter settings reflecting a wide range of hospitals, can be taken into account [101]. To increase scientific relevance, a comparison of the performance of the proposed approach with the performance of relevant approaches in the literature can be made.…”

Organizing integrated processes in cancer care

“…To increase the practical applicability, a comparison of the proposed approach with the current hospital practice can be made, using historical data from a hospital. To increase generality in a wide range of healthcare institutes, not only the specific parameter settings of one hospital, but also a wide range of other possible parameter settings reflecting a wide range of hospitals, can be taken into account [174]. To increase scientific relevance, a comparison of the performance of the proposed approach with the performance of relevant approaches in the literature can be made.…”

“…The lack of multi-disciplinary appointment planning benchmarking instances is in line with the lack of benchmarking instances in healthcare scheduling in general. Only a few benchmark sets are known for healthcare scheduling problems, such as a patient admission scheduling set [60], and a surgery scheduling set [174]. Nurse scheduling is an exception, as many nurse scheduling benchmark sets are available, and most authors benchmark their approaches against the existing literature.…”