Evolution and Process Improvement

The health care goals of increasing revenue, reducing cost, enhancing patient care, and improving customer satisfaction are difficult to achieve due to high costs of integration of legacy systems, cultural barriers to adoption, and the intrinsically complex nature of health care processes. Health care processes typically require deep knowledge and expertise, are highly error prone, and demand a significant requirement for cross-functional workflow [13]. These issues present many barriers to adoption of new technologies.

Research results from the Operating Room of the Future project at the University of Maryland, valuable insights from the business school and computer science departments of the University of Tilburg in the Netherlands, and feedback from the annual Future of Health Technology Summit at the Massachusetts Institute of Technology have led to the development of a process for technology introduction that maximizes probability of successful adoption by minimizing disruption of ongoing surgery operations. We call this process a RECIPE for REal-Time proCess ImProvement in health care [36].

RECIPE focuses on identifying bottlenecks in current processes that can lead to development of small incremental improvements. Most opportunities for intervention can generate 30% improvement, and 100% improvements are often achieved [34]. Strategically introducing small incremental changes into current processes can evolve over time into major institutional transformation.

The RECIPE for incremental evolution of administrative and clinical processes consists of a planning component, a testing component, and a technology component.

The planning component is the responsibility of clinical domain experts and requires:

• High-level mapping of end-to-end health care processes

• Prioritizing opportunities for operational or clinical improvements

• Detailed process mapping of selected process improvement areas

• Selection of precise mechanism for process improvements

• Establishment of research objectives and collected of data for outcomes analysis

For example, the flow sheet below shows a high level mapping of the patient visit needed to prepare for surgery. Other data collected show that some steps in this process are not completed in time or not completed at all prior to surgery, causing delays. More detailed analysis of exactly what happens at delay points allows capture of baseline data and selection of specific actions for process improvement. When a process improvement is introduced, follow-up data is compared to baseline data based on a research protocol established prior to the study. Formalized collection and analysis of research data allows validation of findings and data quality required for publication of research results (Fig. 8.2).

Fig. 8.2 Perioperative process map of patient preparation visit prior to surgery at the University of Maryland Medical Center data prepared under contract with Perioptimum on 24 May 2004

The testing component requires:

• Communication and validation of planned improvements with all stakeholders

• Manual introduction of the process change in a targeted area of the institution

• Data collection to document the effect of the process change

• Recommendations for automation of the process improvement

The technology component requires domain knowledge and expertise in new technologies areas. Specifically:

• RFID technologies need to be evaluated, pilot projects initiated, and data collection and monitoring strategies need to be defined.

• Workflow engines are increasingly embedded in ERP and CRM systems for specifying, automating, and updating operational protocols. This technology needs to be evaluated, selected, and fine-tuned for health care operations.

• Alerting and messaging systems need to be implemented to reach any member of the clinical or operational staff on any available device.

• Service-oriented architectures (SOAs) need to be deployed to provide cost-effective integration with enterprise and departmental systems.

• An operational database needs to be established to store protocol specifications, state of protocol executions, and essential data collected as part of a medical encounter record (MER) that is used for workflow execution.

• Dynamic real-time application generation of workflow requests for action or information is specified and implemented.

• Automated reporting is essential for real-time operations information and data mining of historical workflow data.

• Monitoring and reporting on changes in baseline data affected by incremental process improvement needs to be automatically delivered to clinical and administrative staff on a routine basis to demonstrate and maintain process improvements.

The focus of all these efforts is to bring knowledge gained in other industries into health care, such as airport logistics [15], where deployment of these techniques has placed Amsterdam Airport Schiphol in the top three airports in the world with respect to the passenger experience. Throughput can typically be enhanced in virtually all operations: inventory can be reduced through just in time delivery, quality can be enhanced, and patient satisfaction of medical products can be optimized through innovation and fine-tuning at the level of individual clients.

Boston Medical Center, the city's safety net hospital, is becoming a model of how to bring relief to the nation's beleaguered emergency rooms, reducing treatment delays and closures to ambulances when ERs are more crowded than ever. BMC emergency doctors are treating more patients than they did last year and have reduced average time in the waiting room from 60 minutes to 40 minutes. The secret lies in a radical idea for medicine, but one that has guided airport managers and restaurant hostesses for years: Keep the customers moving.

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