General
As proposed by Spohrer and Demirkan,1 the idea of “integrating scientific, engineering, and management disciplines to innovate in the services that organizations perform to create value for customers and shareholders that could not be achieved through disciplines in isolation” produces impressive results when there is an approach that orchestrates innovation and value generation. Our version for this integration is what we call Business Engineering, which is a hierarchical top-down approach allowing systemic design of a complete enterprise, or part of it, starting from Strategy and Business Model, putting them into practice by determining needed Capabilities and Business Design, and finishing with processes and IT support design that make the whole innovation operative. The proof that this approach works is empirical, with its application to many real cases, successfully implemented, in very different situations in manufacturing, distribution, financial services, government services, and health, both private and public.
Another important conclusion is that it is possible to continuously formalize and structure design experience in patterns—business, architecture, and processes—that provide innovation design ideas at different levels of the proposed design hierarchy. We have shown in all the cases presented that innovations are accelerated when a design pattern is used, specializing it to the particular situation, which is much more efficient and effective than to start design from a scratch or from so-called as-is models.
The use of a hierarchical design approach based on patterns has also the advantage of allowing to position at the right place the integration of Spohrer and Demirkan’s disciplines when performing service innovations. Thus, for example, as we have presented in this book, quantitative marketing—with the tools of Data Mining—is used to model customers needs and options; Management Science allows characterizing providers’ logistic; Economics theory permit to model competitors’ behavior; knowledge management and change management define people’s roles in service change; Industrial Engineering and Information Sciences provide the tools for information analysis and supporting IT tools definition; and all these disciplines as well as Strategic Planning, Analytics—as Optimization Models and Business Analytics—process modeling and design, project management and others serve as a basis to generate ideas to produce and implement a design that realizes and innovation that generates value for the customers and stakeholders.
Students at the Master in Business Engineering (MBE) at the University of Chile develop projects as part of their thesis; then all the knowledge and experience gathered from the cases, some of which have been reported in this book, are being continuously incorporated into existing patterns as well as new specialized ones for domains such as health. This means that reuse of knowledge incorporated into the patterns is increasingly used in areas of new applications and this produces a virtuous circle: knowledge generated enrich the patterns and permit generating new ones. Additionally, this creates the possibility that, when very detailed patterns exist in a domain, general software solutions can be developed to support the detailed process patterns. Subsequently, general flexible solutions—including business, processes, and software design—can be developed for a domain, which can be specialized to particular cases. The advantage of this approach, as compared to ERP, is in the feasibility of customizing the whole design, from business to software, with reasonable effort, especially when this is combined with process execution, using BPMN models and a Business Process Management Suites (BPMS). We have already shown that this approach is feasible in the health domain; a detailed conclusion of the same is stated in the next section of this chapter.
One last general conclusion concerns the power of a well-defined design approach as the one we propose, with appropriate tool support, to make possible that students with little or no experience generate solutions in complex cases in a short period of time. This has been the situation in all the cases in the health sector as reported, with results that will be elaborated later.
Health Care Cases
The case of the private hospital, reported in Chapters 3 and Chapters 4, reinforces the conclusions of the previous section that there is a large potential for service innovation in all kinds of businesses. In fact, the hospital, in this case, which is recognized as one of the best in Latin America, completely reformulated its Strategy and investment initiatives generation and evaluation by means of a design that assures developing new services that maximize value for the stakeholders. Thus, the hospital maintains its competiveness by the continuous services innovation that is generated by well-designed processes and supporting systems.
In the public hospital cases, presented in Chapter 3 to 6, value has been generated in several dimensions: quality, efficiency, and fairness. These objectives, defined in detail in Chapter 2, are related to ideas proposed by Porter and Teisberg and Christensen et al.2 The important result here proves that very significant improvements can be obtained in all these objectives by using the approach we propose for service design. In particular, we have stressed the increase in quality and fairness that can be generated in public hospitals, with no additional resources, by designing service to patients taking into account all the relevant variables, as opposed to current emphasis on the reduction of waiting lists ordered by time of first medical service request. We have proved that this is basically a wrong approach, in which government has wasted tens and possibly hundreds of millions of dollars to reduce such lists, by giving extra resources, which can be spent in private hospitals, to eliminate the patients who have waited a longer time since they entered the lists, in a first-in-first-out (FIFO) rule. The solution we have designed and implemented in various hospitals is to prioritize patients according to type of pathology and aggravating factors, which means, as proposed by Porter and Teisberg and Christensen et al., to provide the right service at the right time. We have actually proved that many patients with severe risk for their lives have been overlooked with current waiting list management and that our solution gives the correct ordering of patients. This has been publicly acknowledged by doctors, who have actively participated in the determination of the rules that define priorities, and also by several hospitals’ directors. Other cases that have provided more quality and fairness are the ones that have allowed designing hospital’s right configuration of services and capacity to insure a predetermined service level for patients. This was done for emergency services and for surgical services. Behind these cases, the idea is also of optimizing the level of resources by providing what is strictly necessary to give the desired service level.
The results on the efficiency objective are extremely important, since we have been able to show that there is a high potential for productivity improvements in public hospitals. We executed work on two fronts to prove this. First, the hospitals’ efficiency was compared with analytical techniques that allowed determining what is called “efficiency frontier,” which defines hospitals that perform better than the rest. Then, for hospitals that perform poorly, typically 20 to 30 percent below, as compared to those on the frontier, investment initiatives can be defined to “move” these hospitals up to better efficiency levels. The important factor here is that the health sector investments can be optimized by acting over hospitals where there is sure return in better productivity. To facilitate this we designed processes that can pinpoint those hospitals and determine the specific performance variables that should be acted upon to ensure results. The second way in which we contributed to increase hospitals’ efficiency was to select key resources and show that they can be used more efficiently. We proved this for surgical facilities or operating rooms, for which we showed that their use can be increased up to 20 percent more than today, under similar conditions and, possibly, up to 50 percent, if resources are better planned and monitoring and control are exerted over them. As a matter of fact, this is one of the key projects on which we are working currently. Also in the line of efficiency we have shown, in Chapter 3, how to manage innovation and change in networks of public health facilities oriented to define projects that improve use of resources and, possibly, increase quality at the same time. Finally, we have presented cases for emergency services, in Chapters 3 and Chapters 4, which are also important in terms of efficiency improvements and also quality. These cases show that configuration and capacity can be designed, using Analytics, to assure a desired quality level and, at the same time, use the right amount of resources; also that the chaotic flow of emergency services can be improved by using predictive models to forecast problems and try to prevent them, and flow monitoring to detect situations that require actions to speed up patients or to avoid wasting of resources.
Another important line of work presented in this book is a preventive approach in chronic diseases. Thus we have shown that it is possible to develop analytical models for crisis’ prediction for this type of disease, allowing preventive actions to avoid health risks. Two cases were presented in Chapter 6: one related to diabetes patients in a private hospital and another for children with respiratory problems in a public hospital, which are treated at their homes; this last case is particularly impressive, since it allowed the possibility of optimizing a solution that is not only good for patients, but also liberates beds at the hospital, which is very scarce resource. Such solution is now routinely working at the hospital.
A complementary factor to the preceding objective has been relevant, which is the speed with which solutions can be developed. For example, we have been able to generate proposed designs for key parts of health service in a couple of months and implemented solutions in less than six months. Among others, solutions already working routinely in at least three hospitals are the design of emergency service configuration implemented with the introduction of a Triage, which has been automated in one case, and a fast-track line; emergency monitoring and capacity planning, resulting in a reduction of patient waiting time; demand analysis for surgery waiting list prioritization, which has reduced the list by about 50 percent and greatly improved the decisions on who is to be operated first, including the identification of many cases of patients who have been overlooked with a risk for their lives. But the most important result is that all the designs we have done so far for hospitals services, which are based on general patterns, are common and can be easily applied to other hospitals with small adaptations, which is facilitated by the fact that they are based on formal BPMN models and can be easily edited in specializing them to particular situations at a given hospital. As a matter of fact, we are already replicating the solutions in hospitals not involved in the initial development.
An extension of the work that we are developing is the execution of BPMN models by using Business Process Management Suits Service Oriented Architecture (BPMS-SOA) technology. We have implemented the patient prioritization in ambulatory services and operating room scheduling using open BPMS technology, described in a case in Chapter 6, and proved that it is feasible to execute the complete pattern workflow, including forms for people interaction with the system, using web services for the implementation of the analytical support and invocations to databases that contain the data needed for execution. The key result expected with these extensions is the capability of incrementing flexibility and rapidity in implementing our general design patterns for services design.
The pattern-based design approach supported by BPMS modeling and execution makes possible that the solutions developed would be eventually used in all Chilean public hospitals.
Finally, the cases for Enterprise Architecture design for public health in Chapters 2, Chapters 3, and Chapters 4 show that it is feasible to do formal design for very complex health structures. Such designs provide a well-founded and economically sound solution for very acute problems, such as innovation to improve quality of service and improve efficiency. This is in the line of several proposals that have been made in the literature to improve health, among others the one presented by a group of experts to President Obama,3 recommendations by Porter and Teisberg4 and findings at the University of Pennsylvania.5 We feel we are contributing with solutions related to such proposals.