Discussion Health Care Quality Improvement Science

March 8, 2022
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Discussion Health Care Quality Improvement Science

Discussion Health Care Quality Improvement Science

Strome, Trevor L., and Trevor L. Strome. Healthcare Analytics for Quality and Performance Improvement, John Wiley & Sons, Incorporated, 2013. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/capella/detail.action?docID=1443907.
Created from capella on 2022-01-18 23:06:17.

Topic: Analytics Strategy Framework, with a Focus on Quality/Performance Improvement
In most healthcare information technology (HIT) initiatives, the information technology (IT) department of an HCO is primarily responsible for the implementation and maintenance of the technology itself (that is, the hardware, software, implementation, testing, and maintenance). The primary users of HIT, on the other hand, reside within the business side of the organization, and it is also the business side that gains benefit and value from having such tools in place. The partnership between the business side and IT in development of an effective BI and analytics infrastructure may at times be at odds, not because of competing interests necessarily, but because each group may not be aware of or fully understand the interests and priorities of the other. Building an Analytics Strategy— Templates To download sample templates and worksheets for developing an analytics strategy within your organization, please visit this book’s web site, http://HealthcareAnalyticsBook.com. Healthcare analytics is not immune to this requirements tug-of-war between IT and the business side of an organization. For example, with
Analytics Strategy Framework, with a Focus on Quality/Performance Improvement In most healthcare information technology (HIT) initiatives, the information technology (IT) department of an HCO is primarily responsible for the implementation and maintenance of the technology itself (that is, the hardware, software, implementation, testing, and maintenance). The primary users of HIT, on the other hand, reside within the business side of the organization, and it is also the business side that gains benefit and value from having such tools in place. The partnership between the business side and IT in development of an effective BI and analytics infrastructure may at times be at odds, not because of competing interests necessarily, but because each group may not be aware of or fully understand the interests and priorities of the other. Building an Analytics Strategy— Templates To download sample templates and worksheets for developing an analytics strategy within your organization, please visit this book’s web site, http://HealthcareAnalyticsBook.com. Healthcare analytics is not immune to this requirements tug-of-war between IT and the business side of an organization. For example, with
clinical applications such as EMRs, the end users are decidedly clinical, whereas IT personnel, who are primarily nonclinical, are responsible for system deployment, support, and maintenance. Analytics development tends to require significant input and participation from both the IT and business side of the organization and should include clinical, data, statistical, application, and technical subject matter experts. With the diversity of skills, knowledge, and people working on analytics for quality improvement and other projects, the analytics strategy helps HCOs: ■ ■ ■ ■ Recognize and agree on the quality and performance goals of the HCO; Determine the best methods for achieving those goals; Identify the analytics required to enable those methods; and Assemble the team, build and/or buy the tools, and implement the techniques necessary to make the analytics work. Figure 3.1 illustrates an analytics strategy framework that incorporates the key components of an effective healthcare analytics system that supports
quality and performance improvement. The areas that should be considered in a comprehensive analytics strategy include: ■ ■ ■ ■ ■ ■ Business and quality context Stakeholders and users Processes and data Tools and techniques Team and training Technology and infrastructure These components of an analytics strategy framework are discussed in the following sections. Business and Quality Context The business and quality context outlines the business problems facing the HCO, and the quality, financial, and performance goals to which the HCO is committing to address those problems. It is essential to start drafting the analytics strategy with a clear understanding of the needs and requirements of the business; without clear guidance from the needs of the business, analytics may not provide the insight and information required to support the evidence-based decision making necessary to achieve the desired quality and performance goals. To this end, all elements of an analytics environment should be aligned in support of the needs of the business. The root of every successful analytical venture in which analytics is actively used throughout an HCO by decision makers is a detailed description of the problem being addressed and a clear articulation of why solving that problem is important to the organization. A well-articulated business problem defines a gap between the current (undesirable) state and the future (more desirable) state. Without a clear and concise problem definition, much effort and resources may be focused on addressing mere symptoms of a much deeper-rooted problem, or on issues that are not really a priority at all. There are many types of problems facing HCOs, ranging from financial pressures to regulatory requirements; problem statements identify which are the most pressing for an individual HCO to address at a given time. The types of problems that HCOs need to address will also direct the types of analytics (and supporting data) required. Some problems typical of those experienced and expressed by HCOs include: ■ Clinical quality. Is the HCO providing the best possible care and diagnostics at the right time, to the right patients, and in the most efficient and safe manner possible?
■ ■ Financial. Is the HCO making clinical, operational, and administrative decisions that are the most financially sound while still in the best interest of the patients? Patient throughput and value. Is the HCO providing value to its patients by minimizing the time they must wait for appointments, assessments, treatments, or other services within the organization, and are they satisfied with the performance and care they experience? Human resources. Is the morale and well-being of the HCO’s staff consistent with HR guidelines and, more importantly, consistent with positive patient experiences? ■ Quality and performance targets are a necessary accompaniment to the problem definition. HCOs cannot possibly improve every process, eliminate every inefficiency, and reduce every risk at once; otherwise, chaos will ensue and nothing will improve. Quality and performance targets define what the current priorities of the HCO are, and help to focus the efforts of quality improvement and analytics teams. The quality goals represent the most pressing problems that have been identified by stakeholders in the organization, highlight what most needs to improve, and indicate the desired or target performance levels. An analytics strategy needs to include the most relevant and important quality goals. This is because an HCO needs to communicate these critical goals to all relevant programs, departments, and units that will be held accountable for their performance. Stakeholders and Users From a project management perspective, stakeholders are “individuals and organizations that are actively involved in the project, or whose interests may be positively or negatively affected as a result of project execution” and “may also exert influence over the project and its results.” 2 Likewise, an analytics stakeholder is a person or group of persons who are impacted by, will be users of, or otherwise have a concern or interest in the development and deployment of analytical solutions throughout the HCO. In a modern HCO there are few people who are not impacted in some way by the use of analytics to improve quality and performance, and there are fewer yet whose roles could not be enhanced through the innovative and effective use of analytics. When developing an analytics strategy, it is important to elicit and document what each of the stakeholders will require, and develop approaches to ensure that their information needs are being met. There are many stakeholder groups within an HCO; analytics stakeholders typical within an HCO are summarized in Table 3.1.
TABLE 3.1 Summary of Stakeholder Types within an HCO Stakeholder Patient Sponsor Description Influencer The person whose health and healthcare experience we’re trying to improve with the use of analytics. The person who supports and provides financial resources for the development and implementation of the analytics infrastructure. A person who may not be directly involved in the development or use of analytics within the HCO, but who holds considerable influence (positive or negative) over the support of analytics initiatives. A person within the HCO who accesses analytical tools, or uses the output of analytical tools, to support decision making and to drive action. Customer/user Patient. The most important analytics stakeholder within an HCO is the patient. The patient is the reason healthcare exists, and is whom we are trying to care for in safer, more efficient ways through the use of analytics. Most pertinent quality improvement methodologies implore quality improvement practitioners not to lose sight of what is the best for the patient. Although it is possible to forget this fact when not working on the front line, analytics professionals must always remember that they are building analytics to directly support the teams that improve the health and in-hospital experience of the patient. Sponsor. The sponsor may be one of the most critical stakeholders in the successful implementation and application of analytics within the HCO. The project sponsor is “the individual or group within or external to the performing organization that provides the financial resources, in cash or in kind, for the project.” 3 This is the individual (or group of individuals) within the organization at a corporate level who approves, or provides a very strong recommendation to approve, the financial resources necessary to implement a viable analytics infrastructure. In many HCOs, the sponsor may be the same executive who recommends and/or approves funding for other IT initiatives. Keep in mind, though, that analytics efforts cross the boundary between IT and the business so there are likely to be clinical, business, and/or technical sponsors for analytics initiatives. Customer/user. From a project perspective, the customer is the individual or group that makes use of a project’s product. 4 Although the customers and users are often synonymous, within a large organization the customer is often the one who pays for the product or work, and the users are the ones who make direct use of the product. The customers and/or users are the
individuals within the HCO who require and use the information and insight available with analytics. It is important not only to know who these analytics users and customers are, but how they intend to interact with the analytics tools and resultant data. For example, will the results of analytics be used to influence clinical decision making, financial planning, quality/process improvement, or for other reasons? Influencers. Influencers are people who, though not directly involved in the development or use of analytics within an organization, wield significant influence over it. Influencers can be found at almost every layer of an organization. It is important that influencers be informed of and understand the benefits of analytics within an HCO. Without the support of influencers at all levels of the HCO, important analytics initiatives may suffer or even be shut down. Nothing is worse for analytics within an HCO than apathy— the thinking that the “same old” data and information is good enough when it clearly is not. An obvious subset of customers are the “traditional” users of analytics— the decision makers, analysts, and quality improvement facilitators. It is not uncommon, however, to see more frontline staff, including physicians and nurses, receive information regarding their performance. In addition, analytics tools are making their way to the point of care as they become embedded in clinical applications, which in turn provides critical decision support evidence and insight to frontline providers when and where it is needed most. Stakeholders classified as users are likely to be the most diverse, and will vary on several important dimensions. Table 3.2 lists several typical customers or users of analytics, as well as a few high-level analytics use cases for each user that are indicative of how analytics will be applied. TABLE 3.2 Sample Analytics Customers with Analytics Use Cases Customer Physician Unit manager Sample Analytics Use Cases Quality improvement team Use real-time analytics for improving diagnostic accuracy. Use personalized performance report to adjust care practices. Determine which patients are likely to exceed length-of-stay targets. Identify bottlenecks in patient flow. Evaluate outcomes of quality improvement initiatives. Evaluate and monitor overall performance of the organization. Executive

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When developing the analytics strategy, it is a good idea to document analytics use cases, or how stakeholders intend to use analytics to make decisions and guide quality and performance improvement projects. Analytics use cases, in combination with goals and objectives of the organization, identify what data elements are most important, what indicators will be necessary to calculate, and what types of accessibility and usability factors (such as dashboard design, configuration of automated alerts, and mobile access) need to be considered. Information elicited from stakeholders to develop analytics use cases should include: ■ ■ ■ ■ ■ ■ Specific problems being addressed by the HCO. Decisions for which analytics insight is required. Actions that are triggered by analytics indicators. Risks that analytics identifies and/or helps to mitigate. What key processes need to be monitored and/or improved. What indicators are required to monitor quality and performance. Obtaining as much information as possible about the possible uses of analytics will help to identify any gaps in analytics capabilities and reduce the likelihood that critical analytics needs will be missed. Strategies for Working Well with Stakeholders Analytics initiatives are most likely to succeed when stakeholders are involved throughout all phases of a project. Here are a few strategies for working well with stakeholders. ■ ■ ■ ■ ■ Identify key members of each of the stakeholder groups. Understand the needs of each stakeholder group, and the needs of members within each stakeholder group. Listen to, acknowledge, and act on the input of stakeholders. Keep stakeholders informed of progress. Deliver on promises made to stakeholders and demonstrate the value of analytics in addressing the stakeholders’ needs.
Processes and Data Accurate, timely, and readily available data is the backbone of all analytics used for decision making, especially in quality and performance improvement projects. Without data, it is impossible to determine baseline performance, use a verifiable decision-making process to decide on improvement opportunities, or evaluate outcomes. Modern computerized clinical systems, such as EMRs, contain dozens if not hundreds of individual data elements; with multiple systems online within HCOs, the potential exists for thousands of possible data items from which to choose. Even if every data item captured from available computerized systems within an HCO is made available via an enterprise data warehouse or other data store, most of this data would require additional processing and analysis to be useful. To make data useful, an analytics strategy must address: ■ ■ ■ How to determine which data is most important for quality and performance improvement. How the data is managed and its quality assured. How the data links back to business processes for necessary context. See Table 3.3 for a summary of strategy components relating to data and processes. TABLE 3.3 Strategy Components for Data and Processes Strategy Component Data sources Issues What are the sources of data available? What data is necessary for the analytics required to address key business issues? What data sources (and data elements) are most important to address financial, quality, and performance issues of the organization? How is data integrated from source systems? How and where is data stored and made accessible to analytics; for example, is there an enterprise data warehouse? How good is the quality of available data? Is the data quality “good enough” for analytics? What gaps in data exist? Does metadata (documentation) exist for the data? ( continued ) Data quality
DATA QUALITY, MANAGEMENT, AND GOVERNANCE Before any analytics are possible, the relevant and necessary data must be understood and made available. Given the many possible sources of data within an HCO, one challenge is integrating data from these source systems into a manageable and accessible framework from which data can be drawn for analytics. These multiple data sources must all be managed to ensure suitability and usability for analytics purposes. Tip Data from source systems must be inventoried, analyzed, documented, and aligned with business processes. Successful execution of an analytics strategy requires relevant data to be identified, documented, processed, and made available to appropriate analytics users and applications. It may not be possible, feasible, or even necessary to account for every available data source. When initiating, or improving, the use of analytics within an HCO, focus on ensuring access to data that is related to the organization’s major quality goals and key business objectives. Trying to encompass too much will only serve to water down the strategy document and risk sullying the insight and information required by stakeholders. Remember that a goal of the analytics strategy is to focus efforts on
achieving the most important quality and performance objectives of the HCO. As the organization’s priorities evolve, so, too, can the strategy document remain aligned with the priorities of the organization. At this point, new business problems and additional data can, and should, be considered. Tip Remember that the goal of the analytics strategy is to focus efforts on achieving the most important quality and performance objectives of the HCO. The quality of data available and used for analytics impacts what information, insight, and value can be derived from such toolsets. Data stewardship is a critical function in the management of large and complex data sets. Improper management of data can lead to BI producing incorrect information. Because the needs of every organization are different, the analytics strategy will help the HCO determine what data management and governance structures are best suited to the HCO based on the extent of existing and future data sources, IT support, and any existing governance structures already in place. BUSINESS PROCESSES One of the other data-related challenges facing HCOs is adding context to data. From an analytics perspective, data and processes are inseparable; knowing what a value “is” is almost useless without knowing what it “means.” Knowledge of business processes provides essential context to and understanding of what data represents. A business process is the collection of actions taken to transform an input (such as raw material, information, knowledge, commitment, or status) into a desired outcome, product, or result and performed according to established guidelines, policies, procedures, rules, and subject matter expertise. 5 The business processes are what provides context to the data, and without context, data is almost meaningless. Essentially all quality improvement methodologies require indicators and metrics that examine intervals on the other process measures. This requires a strong alignment between business process components and the data that measures those components. As part of the analytics strategy, you should consider if and how current business processes are documented, and how data items are mapped to these documented business processes. Tools and Techniques Once the business problems, quality goals, stakeholder requirements, and available data items have been identified, the necessary tools and
techniques, plus their acquisition strategy (build versus buy), need to be outlined in the strategy. Selection of appropriate software, statistics, or models is necessary to ensure that the “right kind” of analytics can be performed to address stakeholder needs and the HCO’s business problems. Inappropriate tools and misaligned capabilities can lead to issues as basic as providing an inappropriate summarization to using a predictive model that does not work with the data available or is inappropriate for the use an HCO was intending. For example, if an HCO is looking to determine its geographic catchment area based on ZIP codes to fine-tune a marketing campaign, that information might be best presented visually using some sort of geographical representation rather than a table of numbers and ZIP codes. Not having the tools to properly visualize data in meaningful ways for decision makers would be a capability gap. Another example relates to advanced analysis; many reporting tools do not include anything other than basic statistics (such as mean, median, etc.). Yet sometimes an analysis needs to look beyond these simple statistics to determine correlation or to implement more complex statistical models. Because there are many ways in which analytics can be used, there are many different types of analytics tools. Several of the most common types of analytics tools include: ■ ■ Statistical. Statistical tools are used for deeper statistical analysis that is not available in most “standard” BI or reporting packages, including correlation and regression tests, ANOVA and t -tests, nonparametric tests, and statistical process control chart capabilities. Visualization. Beyond the static charts and graphs typical of almost all spreadsheet and business analysis software, some analytics users are looking for advanced visualization tools that allow them to interact visually with and explore data that is dynamic (that is, the visualizations update as the data is updated). Data profiling and quality. Because the volume of healthcare data is growing, HCOs are increasingly relying on software to identify and highlight patterns of good and poor data within a data set, and to help fix and prevent instances of poor-quality data. ■ If an HCO has invested significantly in a BI infrastructure, there may not be much money available for analytics-related capabilities beyond what comes with the BI suite. Adding new and specialized tools to the analytics tool belt can become cost-prohibitive (especially when expensive “value-add” modules of already expensive base software are required). The good news is that there are very good open-source tools such as R (www .r-project.org) that can provide significant analytical horsepower without a prohibitively high price tag.
One challenge of developing an infrastructure to support analytics is that analytics requirements will undoubtedly expand as more data sources are added, new problems and issues confront the HCO, new analytical capabilities are required, and new hardware and software systems, optimized for analytical performance, emerge on the marketplace. Scalability, which “allows us to maintain a consistent level of performance regardless of changes and growth,” 8 must be built into an analytics infrastructure so that the HCO has spare capacity to grow into as the amount and types of data, as well as analytics needs of stakeholders, continue to evolve and expand. Although the analytics strategy may not necessarily state what technical infrastructure should be acquired, the strategy should make it clear what the nearand long-term analytics needs of the business are going to be. The decisions made regarding hardware selection and infrastructure design and configuration essentially set the boundaries for what analytics will be capable of within the organization. The analytics strategy can be a very important input to the HCO’s overall technical strategy— the sooner that analytical requirements can be incorporated into an HCO’s IT development plan, the less likely it is that technology will be purchased that is not appropriate (either insufficient or complete overkill) for the analytical requirements of the organization. Developing an Analytics Strategy Developing an analytics strategy is critical to ensuring that the analytical needs of an HCO are being met. Most HCOs will not be starting from square one, however. In all likelihood, there are many pockets of analytical knowhow throughout every HCO, suffering through some inadequacy in analytics capabilities and not living up to an analytics potential. For example, some of these analytical pockets may be using outdated or inadequate tools for data management or analysis, some may be reaching the limits of poorly designed data warehouses, and others might simply be so overwhelmed with report requests that they are unable to perform any “real” analytics. An analytics strategy is the starting point to help organizations achieve maximum benefit from their data. A completed strategy will help an organization identify what it does well, what it needs to do better, where it can consolidate, and where it needs to invest. The three main steps in creating an analytics strategy are: 1. Document the current state. Review the six main components of strategy discussed above, and speak with stakeholders who are current (and potential users) of analytics to identify how analytics is currently used and what capability is required but does not yet exist, as well as what exists now but can be improved.

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