This chapter briefly discusses and classifies overall research done in the areas of hospital service delivery modelling, simulation-based analysis and system improvement based on identifying and reducing unwarranted variations. It also discusses the critical research challenges, which are identified as limitations of the current state-of-the-art approaches. This thesis will address each of the limitation that is identified in this Chapter.
Table 2.1 broadly classifies literature in the area of analysing unwarranted variations in the healthcare services at two levels that affects patient care, namely:
public-health level and service delivered system level in hospitals. Unwarranted
variations at public-health level is due to care that is inconsistent with: (i) patient preferences for a particular type of care; or (ii) patient treatment needs and generally deal with identifying unwarranted geographical variations in public spending and healthcare activity outcomes (Rightcare, NHS 2010; Wennberg, 2002; Appleby, et al., 2011). Series of maps or atlases are developed to highlight the variations in various clinical areas of national importance to search for unwarranted variations and tackle the causes and drivers of these variations (Rightcare, NHS 2010; Wennberg, J.E., 2002; Appleby, et al., 2011). On the other hand, unwarranted variations at
system requirements conformance; and (ii) system constraints and can be classified into: (a) unwarranted variations on a care pathway (for example, delays and bottlenecks); and, (b) unwarranted variations due to patient getting unnecessarily diverted from a care pathway (for example, patients routing issues from Emergency Department).
The proposed research deals with analysing unwarranted variations at the service
delivered system level originating within the hospital. Literature, in the area of
analysing unwarranted variations at the service delivered system level in the hospital services, is discussed based on approaches that are extensively used to improve service delivery systems such as – (i) process mapping; and, (ii) simulation modelling.
We will first discuss the literature in the context of (i) process mapping; and, (ii) simulation modelling; and then provide a specific context as it relates to (a) unwarranted variations on a care pathway; and, (b) unwarranted variations due to patient getting unnecessarily diverted from a care pathway.
The process mapping/modelling is defined as a set of activities for identifying various actions and interactions involved in a particular service delivery processes and its visual representation for process visualization and re-design. Simulation modelling refers to modelling and developing dynamic models of the service delivery system for analysing/estimating the service delivery performances against various “what-if” scenarios.
Additionally, these approaches utilize various types of service delivery data for modelling and analysis. These service delivery data are: (i) qualitative data; and, (ii) quantitative data. The qualitative data of the service delivery process is mainly gathe-
Table 2.1: Classification of state-of-the-art approaches for healthcare process improvements
Key Issues Reducing Unwarranted Variations on a Care
Pathway
Reducing Unwarranted Variations from a Care Pathway
Scope
Service delivery data used
Approach Objective
Qualitative data Quantitative Data Qualitative data Quantitative Data
Procedural data Systematic Knowledge Acquisition Historic Data/EPR Tracking Data Procedural data Systematic Knowledge Acquisition Historic Data/EPR Tracking Data Public Healthcare Variations Data
Analysis Geographic Variations - -
Wennberg, (2002); Appleby, et al., (2011); Rightcare, NHS (2010) - - - Wennberg, (2002); Appleby, et al., (2011); Rightcare, NHS (2010) - Services Delivery System level Process Mapping Static Analysis (Process visualization, redesign) VSM (Rother & Shook, 2003, Dickson et al., 2008, Jimmerson et al., 2005) - (Crabbe, et Flowchart al., 1994) IDEF (Staccini et al., 2006) - - - - -
Service Delivery System Modelling based on
RAD(Chapter 4)
Role Variations Modelling based on Tracking Data
(Chapter 6) - Simulation Modelling Dynamic Analysis Quanti- tative data Historic Data Molema, et al., 2007; VanBerkel and Blake, 2007; Bayer, et al., 2010; Dodds, 2005; Connelly & Bair, 2004; Brailsford, et al., 2006
Discrete Event Simulation (DES) Modelling Integrated with Accurate Service Delivery
System Model (Chapter 5)
Pathway Variations Analysis (PVA) Modelling (Chapter 7) - Tracking Data Miller, et al., 2006; 2008 - - - -
-red from clinician workshops, group debates, brainstorming or medical staff interviews. The qualitative data of the service delivery process is further classified into procedural data and systematic knowledge acquisition (KA). Procedural information is mainly related to how things are done, which is essential to any process mapping/modelling approach to develop process models. KA refers to the process of gathering and documenting the procedural information for accurate process mapping. Some of the commonly used methods to identify procedural knowledge, i.e. KA are based on clinician workshops. However, KA to model complex healthcare services of a hospital based on clinician workshops can be challenging. This is partly due to the fact that some of the important information about the service can be missed during simultaneous discussions with multiple staff; thereby resulting in gathering incomplete procedural data for accurate process modelling. Therefore, a process mapping/modelling methodology to accurately model service system is detailed in Chapter 4 which proposes systematic KA approach to effectively gather and document procedural data.
There are various types of quantitative data, which are available in service delivery systems, such as historic system data, electronic patient records (EPR), real time tracking data. This information is generally used for developing various types of simulation modelling for analysing unwarranted variations on a care pathway (see Table 2.1). The behaviour of the service delivery systems under varying system parameters and scenarios can be studied using simulation modelling. It enables process improvement experts to simulate various improvement scenarios that do not yet exist for analysing unwarranted variations occurring on a care pathway. Although, simulation modelling is identified to be useful technique for suggesting service improvements; however, traditional simulation models uses simplified flow
diagrams, which are unable to represent complex collaborative healthcare services. Furthermore, traditional simulation models relied mainly on historic system data. Thus, outputs from traditional simulation models are often unrealistic and generally less than 10% of the process improvement studies involved any simulation modelling tools (Hulpic, 1998). Therefore, a simulation modelling methodology based on accurate service system model complemented with other quantitative data such as EPR, historic data, and real-time tracking information is proposed for analysing unwarranted variations on a care pathway in Chapter 5.
Most of the healthcare process improvement approaches in literature involves analysing unwarranted variations on a care pathway. However, there are significant unwarranted variations from a care pathway. Largely, these variations are unnecessary and leads to lowering of the efficiency and effectiveness of the services delivered. Traditional approaches, relying on modelling & simulating variations on a care pathway, are unable to address the problem of reducing unnecessary variations
from a care pathway. There are limited research studies that deal with the analysis and reduction of unwarranted variations from healthcare services delivered in hospital for overall system level improvements. Therefore, a modelling methodology for service variations such as role variations is proposed in Chapter 6, which utilizes service delivery process model and real-time tracking data for modelling frequently occurring service variation patterns.
In the literature, reducing unnecessary variations by standardizing care delivery processes in healthcare systems or developing & implementation of integrated care pathway (ICP) is identified to be an effective approach (Kitchiner and Bundred, 1999; Pearson, et al., 1995; Wilson, 1998; Archer, et al., 1997; Wentworth and Atkinson, 1996; Willis, et al., 2000). Integrated care pathway (ICP) is a structured
multidisciplinary outline of anticipated care plans which details the steps in the care of patients with specific clinical condition or set of symptoms (Campbell, et al., 1998). ICP generally involves multidisciplinary communication among several speciality units in hospital to efficiently provide care to patients. Hence, several research studies have worked on creation of ICPs (Kitchiner and Bundred, 1999; Pearson, et al., 1995; Wilson, 1998; Archer, et al., 1997; Wentworth and Atkinson 1996; Willis, et al., 2000). As a result, ICPs are implemented in predominantly used in hospital to reduce and control care delivery variations in providing care to patients (Panella, et al., 2003). However, effectively implementing ICPs in hospital is often associated with problems due to large variations from care pathway. These variations
from care pathway are largely unnecessary and lead to longer waiting times, delays, and lower productivity of care pathways. For example, patient diversion from care pathway to non-specialty medical units, which compromises the care delivered to patients, is a type of unwarranted variations from care pathways. Therefore, pathway variations analysis (PVA) methodology is proposed in Chapter 7 to identify, model and suggest improvements which reduces unwarranted patient diversions or variations from care pathway leading to performance improvement. The PVA methodology involves accurate and scalable RAD models of care pathways together with EPR and historic data to identify & simulate variations from care pathway for productivity improvements.
Detailed discussions and literature on each of the limitations, discussed in this Chapter and illustrated in Table 2.1, is presented in Chapter 3.