Towards Clinical and Operational Efficiency Through Healthcare Process Analytics

Introduction

Recent years have found healthcare organizations to be placed in the eye of an information overload storm, which is further exacerbated by the proliferation of clinical information systems, electronic health records and network-enabled health devices (Cortada et al., 2012). However, no matter how data-rich healthcare industry has become, it still remains knowledge-poor as the abundance of data that bombards healthcare professionals, lumped under the term “big data”, has complicated their ability to glean useful insights from them which could drive clinical and operational improvements. This becomes more obvious as evidence is continuously mounting that, nowadays, healthcare industry is increasingly being challenged by entrenched inefficiencies, while, at the same time, clinical outcomes remain suboptimal (increased deaths from conditions considered amenable to healthcare, high levels of preventable errors, etc) (Cortada et al., 2012). In this context, healthcare providers are pushed to focus anew on seeking for means to achieve and influence desirable outcomes.

Over the past few decades, business processes have become increasingly important in healthcare enterprise, as they guide healthcare delivery and, as such, they constitute key drivers behind critical success factors, such as cost containment and healthcare quality improvement. However, as healthcare organizations reach higher levels of business process management (BPM) maturity, they often find themselves maintaining very large process model repositories, not being able to exploit the valuable knowledge these represent about their operations (Baquero, 2013). The last few years, big data analytics has emerged as a key business driver for service providers and enterprises as they transform into the digital era. In particular, business process analytics tools have been developed which can be used by businesses to manage, mine, and monetize vast stores of unstructured event data generated by business process executions on large and complex supply chains and more. Thus, several sectors can move away from the traditional baseline of transaction monitoring using basic reporting tools, spreadsheets and application reporting modules and toward a model that will eventually incorporate predictive analytics (Cortada et al., 2012). For the healthcare sector in particular, such a transition could enable organizations to “see the future,” create more personalized healthcare, allow dynamic fraud detection and predict patient behavior (Cortada et al., 2012). Although, currently, healthcare providers consider business process analytics their top investment priority, significant barriers remain to effective use of analytics tools. The biggest issues include having unstandardized data in silos, lack of technology infrastructure and data and analytics skills gaps.