Management of Healthcare Processes (Effective Informatics of Digital Resources through Management Theory)

Management and economic concepts can play a key role in enhancing efficiencies in the industry given the strategies that address the allocation of the correct resources to the demand for those resources. With the aid of accurate and timely information, the ability to communicate and apply that information, physicians, nurses, technicians, administrators can improve the process of caring for those in need in a more efficient, less costly manner. Techniques and methodologies that create, disseminate and analyze data comprise the realm of informatics.

Pure investment in information technologies or digital processes is not the final solution to enabling providers to operate more productively however. Applications in management theory are essential to ensure effective implementation and utilization of these technologies to best leverage their capabilities to increase labor and process productivity. Management theory that must be considered includes:

■ project management

■ knowledge management

■ strategic management

■ management information systems

■ data science, analytic techniques and decision support

These all must be considered in the realm of risk management as well. These management concepts focus on the acquisition of digital technologies that provide the correct functionality to facilitate a particular process and proper implementation of the technology to ensure its appropriate utilization. This includes creating a receptive culture within the organization by users to adopt the platform as an essential tool to enhance their daily routine. Theoretical concepts also address analyzing, communicating and best utilization of the results and outputs of systems that are used.

Project and Knowledge Management

Project Management addresses methods that support successful implementations of information technologies. It addresses the incorporation of correct tactics to acquire the most appropriate technology platform to facilitate an organizational need in the most seamless way possible. For example, when a healthcare provider wants to implement a new telehealth based system that facilitates effective communication between physicians/care providers and patients, the organization must consider which technology best offers the most feasible functionality corresponding to the operational structure of the organization relative to the platforms utilized by patients. This includes the cost of the technology, scalability, data security, ability to integrate, with existing systems, both within the organization and with other systems, as well user friendliness. Once the technology is chosen, factors to promote the most seamless integration into the work environment must be considered. This includes timing schedules, training for users and eventual complete roll out to the workforce.

Knowledge Management theory overlaps with project management to some extent when considering the implementation stages of new technologies. It addresses the additional critical factors of ensuring the systems’ adoption by users with the ultimate goal that it becomes a key component to the every day activities of its users and stakeholders. Knowledge management also addresses those factors that support leveraging the output produced by users working with systems and the dissemination and collaboration of corresponding information among individuals connected to processes and procedures. In other words, KM theory promotes the active utilization of information and creation of knowledge within an organization, concepts that drive best practices and innovation [2,3].

Other management related concepts also must be considered in attempting to best leverage information technologies and digital components. These involve such strategic initiatives as: LEAN/Six Sigma, Supply Chain Management Workflow optimization and advanced analytics including the realm of data science to not only analyze past data patterns, but offer a platform for predictive analytics. The process of treating a patient incorporates a network of activities that are complementary and interdependent in nature, where breakdowns in aspects of one operational entity can cause disruptions to the overall process of patient care. The patient treatment process can include diagnosis, prescription of medications, radiology and lab tests, administration of treatment procedures, monitoring of results and outcomes, etc. These activities include input from numerous personnel in corresponding operational departments in the healthcare organization. Workflow analytic methodologies must be considered to better understand the efficiencies of the entire treatment process. The overall process can be compared to managing a supply chain or supply network of activities that are complementary and interdependent, with the ultimate objective of achieving the best allocation of available resources to provide the best care to corresponding patients. These management methodologies can be augmented with the incorporation of statistical and quantitative based analytics that provide decision support.

Analytics and Data-Based Decision Support (Data Science, Six Sigma, and Data Mining)

Six Sigma is an analytic method that leverages available data resources and incorporates statistical applications and visual capabilities to monitor process variance and efficiencies. By analyzing data resources corresponding to various operational processes with the utilization of statistical techniques, analysts can better determine which types of practices result in unacceptable variances in performance metrics [4]. Lean Six Sigma is a strategic initiative that complements the variance identification focus of six sigma and seeks to eliminate non-value add processes or activities in an organization that can add to waste. Value stream mapping is often used to enhance throughput of processes. For example, a lean six sigma approach could be applied in a radiology application. Are there bottlenecks or unnecessary steps that can be improved upon in the network of activities in the radiology department that produce high delay variances in getting x-ray results back to an attending physician?

More robust and sophisticated techniques to analyze data involve the utilization of quantitative methods to process data and statistical testing to determine patterns and trends that may exist in particular service activities. Data science based methodologies including traditional regression and other data mining methods including AI, enable analysts to better identify reoccurring trends in the various activities of healthcare services. Resulting models can determine whether particular treatment procedures result in enhanced health outcomes according to patient populations, whether particular procedural activities result in unacceptable outcome measures, identify the potential population that is likely to develop chronic illnesses, etc.

These types of analyses are becoming increasingly important as “patient experience” feedback is now a parameter for the new concept of “value-based reimbursement’. A significant percentage of the potential payment adjustment for a provider is predicated on feedback that patients share, so being able to directly address likely patient “dis-satisfiers” provides essential information to address components that drive dissatisfaction.

Business Intelligence and Decision Support Systems

Other methods of data analysis that can help increase the knowledge of healthcare practitioners involves the more simple creation and presentations of reports and graphics through data management capabilities including software applications such as Tableau or Sequel and R coding initiatives. The advantage of these methods focus on presenting data in a timely and understandable manner, where decision makers can quickly view these analytic platforms to identify factors impacting operational performances. Other technologies in the data science spectrum involving AI and natural language processing that enhance information query/search, retrieval and display, provide vital decision support as well. Best practices or standard treatment guidelines relative to patient symptoms and diagnosis can be enhanced and expedited.

These various data driven software technologies and initiatives comprise the realm of decision support systems and business intelligence. The functionalities of the components including report generation, trend and pattern detection, quantitative and statistical based methods, complemented with graphical interfaces, provide users/decision makers in various functional areas of healthcare organizations with timely, actionable information to enhance strategizing. Informatics to improve efficiencies include optimizing resource allocations corresponding to a variety of activities and procedures. The utilization of decision support systems and business intelligence that leverage essential data resources can ultimately help reduce lag times in patients waiting for treatment, adjust treatment procedures to enhance outcomes, reduce inefficiencies in billing, reduce lag times in lab and radiology exam completion and reporting times, etc. The ultimate result is the potential of better management of healthcare operations and costs along with care effectiveness and outcomes for patients.

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