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CONCLUSION
Engaging a Critical Eye
Analysis—how data is turned into information for better decisions—takes looking at the right data and using the right tools, determined by careful forethought and possibly experimentation to ensure that useful, actionable information is brought forth. It is easy to go astray, because many things seem to speak so simply and loudly. One must exercise caution and engage a critical eye in looking at the issue or opportunity to ensure that points of real importance are not missed
Analysis, Analytics and Business Intelligence
Analysis
At its core, all of this work is analysis—that is, separation of a whole or complex idea or topic into its component parts, in an effort to gain a greater understanding. These techniques have been applied in the study of mathematics and logic since before Aristotle.
Analytics
The science of analytics—processes for separation and/or manipulation of data (simultaneously applying statistics, mathematics, operations research, and computer programming) to develop greater understanding of a more recent idea. Its roots are, by extension of the above, a branch of logic dealing with analysis. Analytics also favors the visual display of information to communicate insights. At a simple level, then, analytics can be considered work that simplifies data to amplify meaning.
Business Intelligence [BI]
BI does the same work of simplifying data to amplify meaning in relation to internal, structured data and business processes to transform raw data into meaningful and useful information for business (operating or market competitive) purposes. It is a broad category of applications and technologies for gathering and analyzing data, transforming it to accurate, current, and relevant actionable information and deploying it to users to be available, just when needed, for the purpose of helping enterprise users make better business decisions. It is imperative that firms have an in depth and inter-related knowledge about factors such as the customers, competitors, business partners, economic environment, and internal operations to make effective and good quality business decisions. Business intelligence enables firms to make these kinds of decisions. As currently configured, this work is/will be performed by those engaged in decision support roles. However, individuals like yourself, becoming further certified or specialized in the discipline will be the future practitioners. Along with this is the likelihood that, this work will become further specialized beyond the decision support function typically seen in organizations today.
Clinical Informatics
This is the application of information science and information technology to the delivery of healthcare services. Clinical Informatics is concerned with information use in health care by clinicians. It is also referred to as applied clinical informatics and operational informatics. Specific to healthcare, yet not completely distinct from most of the aspects previously described in BI. Much of the analytics work and output in healthcare will be through collaborative efforts of BI/Analytics practitioners and Clinical Informaticists.
AMIA (American Medical Informatics Association) considers informatics, when used for healthcare delivery, to be essentially the same regardless of the health professional group involved -- whether nurse, physician, pharmacist, imaging technician or other health professional.
Clinical informatics includes a wide range of topics ranging from clinical decision support to visual images (e.g. radiological, pathological, dermatological, ophthalmological, etc); from clinical documentation to provider order entry systems; and from system design to system implementation and adoption issues.
[https://www.amia.org/applications-informatics/clinical-informatics]
A prominent and growing group of clinical informatics practitioners are Nursing Informaticists.
Nursing Informatics is the science and practice that integrates information science and nursing science, its information and knowledge, with management of information and communication technologies to promote the health of people, families, and communities worldwide. The application of nursing informatics knowledge is empowering for all health care practitioners in achieving patient-centered care.
Nursing Informatics (n.d., para.1). In AMIA.org. Retrieved from https://www.amia.org/programs/ working-groups/nursing-informatics. For more information, visit this site.
Analysis, Analytics and Business Intelligence
BI is a set of methodologies, processes, architectures, and technologies that encompass the three types of analytics. This module will walk through this emerging discipline as applied to the business of healthcare service delivery—what are the types of analytics, what are the simplifications, what are the meanings that can be derived, what are the benefits of this work, and what is the emerging work that couples analytics and decision management to drive better decision processes in the delivery of health care?
Three types of analytics are presented in this course. BI as a discipline continues to evolve, and one may find other ”types”presented elsewhere, however these three types of analytics are foundational and are most straightforward related to organizational action in service to customers.
Simply put, these types of analytics denote what happened, what could happen, and what one should do about it. They are not necessarily used in lock sequence. As the diagram portrays, they all should be used as a continuing sequence of stages, tasks, or events that can occur in any direction. Data limitations and unaccounted-for external forces, among other issues, can distort or derail output produced by any of these.
Descriptive Analytics
Descriptive analytics provides a look at, and possibly an understanding of, past performance. This understanding is unlocked from the historical data by summarizing and comparing or mining for patterns. The patterns then may point to the reasons for success or failure. Patterns in data then are described through the presentation of data in the form of tables and charts. Almost all management reporting falls under descriptive analytics. It is the predominant analytic type in use, mostly because it has been used for many years and does not require extensive computational power to yield significant results.
Summarizations and comparisons are most often made by means of the following with readily available tools, such as Excel.
frequency distributions and standard deviations
scatter plotting
trend lines
Predictive Analytics
The practice of relating what you do know to what you do not know, provides better information that is valuable at the point when a decision is needed. Here, probabilities are applied to historical data in combination with rules, algorithms, and possibly external data to determine the probable outcome of an event or likelihood of a situation occurring.
Indicating the probability of occurrence
pointing out possible actions to address the patterns
indicating possible consequences of action or inaction
In sum—one can turn uncertainty (what one does not know) into usable probability—that is, take action based on the likelihood of occurrence.
Predictive analytics are variously presented as numbers, scores, or percentages, depending on the use of the information – for example, checking a credit score before a costly elective-procedure admission, or the number of labor hours needed on Tuesday.
Prescriptive Analytics
Prescriptive analytics is a very new and still-emerging area that uses hybrid data (which might be any of the following: historical, real-time, internal or external, structured or unstructured, and/or others) along with business rules and mathematical models to indicate what should be done. The models may or may not include a predictive component, and thus, prescriptive analytics is not necessarily predictive.
Decision paths or options can be presented or decision implications illustrated using a “what if” analysis. As with predictive analytics, output can be continuously generated and updated to take advantage of new and better information as it becomes available.
Types of Analytics—Simplifications Provided
As alluded to on the previous pages, these types of analytics provide simplifications that are easy to understand and use for business decision-making. We’ll first discuss descriptive analytics.
Descriptive Analytics
Descriptive analytics is generated by querying, reporting, and online analytical processing (OLAP) tools and techniques that can help answer the questions about:
Data (information) is presented in terms of:
What happened? (through general reporting)
Why is this happening? (by showing patterns or trends)
Predictive Analytics
Predictive analytics refers to the skills, technologies, applications, and practices for exploration that answer the questions:
These can be generated continuously and or iteratively, as in performing“what if” scenarios.
Predictive analytics specifically applies inferential statistics, probability testing and confidence intervals in the analysis.
The chart here depicts the historical volume as drawn from the organizational data (dark blue line) and an accompanying trend line (yellow line). Superimposed over the historical line is a simulated forecast (pink line) showing how well a forecast can track reality. The red line on the far right of the chart is the prediction (forecast) given the historical data. Note the trend (yellow line) is less precise than the prediction.
Key Points to Remember
As you move on, please keep in mind the following points.
The interconnection relationship between various processes and tools used in business intelligence analysis are as follows:
Key Points to Remember
As you move on, please keep in mind the following points.
An experiment provides insight into true cause-and-effect by demonstrating what outcome occurs when a particular factor is manipulated.
A sample is the data set used to make inferences about the entire population.
The common mistakes made in business analytics are as follows:
Sophistication compensating for a lack of data
Difficulty isolating and explaining patterns shown by data
Equating correlation with causation
The six steps that comprise the prioritization matrix method for high-level sorting-through are as follows:
Step 1: Develop a list of items in the following categories, which also can be classified as within an ASAA.
Step 2: Each characteristic on the left should be weighted as a fraction of 1.0, so that item ratings can be compared.
Step 3: Rate each item's performance related to each characteristic, with 1 being low and 5 being high.
Step 4: Multiply the ratings by the weight to obtain an item score.
Step 5: Compare scores to see priority focus areas.
Step 6: Be sure analytics of all three types are developed and in use.
BI/analytics consultants will be asked to address situations like that in the following example. It is an iterative process of creative analytic thinking—preparation/immersion/incubation/insight—that the BI/analytics consultant must engage.
Directly linking and recognizing interplay between healthcare user condition, service location, cost, and coverage are critical resource allocation issues that need to be addressed outside of the clinical services delivered
