CSBI Course 3: The Discipline of Business Intelligence Introduction Business Intelligence Defined: Types of Analytics Business Intelligence Basics Business Intelligence Pr

The Discipline of Business Intelligence(Course 3)

Introduction

This section provides an overview of the discipline of business intelligence, as applied to the business of healthcare service delivery for the improvement of decisions. It includes the types of analytics and examples, timeframe based viewpoints that engaging analytics provides and presents these viewpoints in relation to decision-making power generated. This section will also discuss the concept of decision management, decision types and analytic tools.

Analysis, Analytics and Business Intelligence

A good deal of discussion and some confusion exists as the science of working with data evolves, especially in healthcare. When there is talk of engaging business intelligence(BI), often a question arises,”What does this encompass?” Various definitions are put forth and another question arises, “How is business intelligence different from business analytics? How does this differ from Clinical informatics being engaged in many healthcare organizations?” And now the water is very muddy. This will require filtering, clarity and foundation.

Analytics The science of analytics-processes for separation and/or manipulation of data(simultaneously applying statics, mathematics, operations research and computer programming) to develop greater understanding is a more recent idea, however, 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.

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 to gain a greater understanding. These techniques have been applied in the study of mathematics and logic since before Aristotle.

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 versus market competitive) purposes.

Clinical Information This is the application of information science and information technology to the delivery of healthcare services. Clinical informatics is concerned with information use in healthcare by clinicians. It is also referred to as applied clinical informatics and operational informatics. Much of the analytics work and output in healthcare will be through collaborative effortsofBI/Analytics practitioners and Clinical Informationists.

AMIA(American Medical Informatics Associations) 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 professionals.

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, from system design to system implementation and adoption issues.*

You, the BI/A consultant, should be working with physician and nursing informaticists (assuming your organization has these members on board). They are heavily involved with electronic health records(EHR) and other suh system implementations and these are experts in clinical/operations workflows which are crucial to understand in performing many analytic activities.

Business Intelligence Business intelligence(BI) is a set of methodologies, processes, architectures and technologies that encompass those three types of analytics. This section will walk through the continually emerging discipline as applied to 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? What is the emerging work that couples analytics and decision management to drive better decision processes in the delivery of healthcare?

Analytics

Now, one might ask: Why analytics? Why spend the time?

The answer is: “Scientific management is moving from a skill that creates competitive advantage to an ante that gives a company the right to play the game.”

-Ian & Elizabeth Stephenson, Ten Trends to Watch in 2006, McKinsey Quarterly, Jan. 2006

Now we can easily see these trends are taking shape more quickly than was thought when the piece was written. These trends apply to all industries, however the trend may be amplified in healthcare now based on the need to achieve significant change in a short period of time.

Section 2

Types of Analytics

The three types of analytics presented in this course are foundational, straightforward and immediately related to organizational action in service to customers. BI as a discipline continues to evolve and other types of analytics are beyond the scope of this course.

These types of analytics denote what happened, what could happen, and what one should do about it. They are not necessarily used in locked in locked sequence. 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 an understanding of past performance. This understanding is unlocked from the historical data by summarizing and comparing or mining for patterns. The patterns may point to the reasons for success or failure. Patterns in data 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 with readily available tools, such as Excel.

● Percentiles ● 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 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 outcurring.

Meaning can be ascribe by: ● Indicating the probability of occurrence ● Pointing out possible actions to address the patterns ● Indicating possible consequence of action or inaction

In sum, one can turn uncertainty (what one does not know) into usable probability and act based on the likelihood of occurrence. Predictive analytics are presented as numbers, scores, or percentages, depending on the use of the information.

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, an thus, prescriptive analytics is not necessarily predictive.

Decision paths can be presented or decision implications can be 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.

Descriptive Analytics Examples ● Count of healthcare users-inpatient admissions ● Cost per adjusted discharge ● Children and adolescents with non-urgent health issues in the ED during primary

physician office hours. ● Average Length of Stay(LOS) ● Days of average LOS above three ● Median days in AR ● Door-to-administration of TPA time measure. To describe the situation, use all the

following: ○ Range of minutes ○ Mean minutes ○ Median minutes ○ Standard deviation in number of minutes

● Number of cased miscoded ● Number of miscoded cases per shift ● Days cash on hand versus target ● Labor compensation ratio ● Inpatient census per hour per unit

● 30-day readmits ● HCAHPS score

Predictive Examples

● Volume demand for services: ED/inpatient ● Labor hours needed next shift ● Respiratory therapist FTEs needed next March ● Probability of ED being over capacity tomorrow ● Number of medication pumps needed per month ● Billings versus collections over the next 30 days ● Unit expenses per month for the next quarter’s flexible budget cycle

Prescriptive Analytics Examples

● One less RN needed next shift ● Order 200 surgical kit packs #2328 for use over the next six months with delivery

scheduled ● Always review any case with code 12345A for coding errors ● Accounts payable schedule ● Services needed per specific admit type XXX to avoid 30-day readmit ● Inpatient transport to imaging on Mondays first shift at 3 FTEs ● Increase flex budget for 3 North unit by 3.5%

These would be the result of calculations performed using the methodologies described rather than ruleof thumb or estimations or guesses after looking at descriptive information, which are used to make these operational decisions in most cases currently.

Types of Analytics-Simplifications Provided

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:

● What happened(through general reporting) ● Why is this happening( by showing patterns or trends)

Data is presented in terms of:

● Quantities ● Frequences ● Locations ● Classifications ● Combinations ● Comparisons ● Trends

Descriptive Analytics Example

The example shown here indicates averages of what happened over a specific time period. This chart invites the consultant to investigates why volumes on days/times such as Thursday 1300-1500 hours are below all other days of the week that are otherwise quite uniformly higher in volume during this time frame. Why is variability on all days in this timeframe except Thursday so limited when all days of the week after 0900 and outside this timeframe widely variable? Further, it invites analysis to determine the reasons for target(s) achieved or missed. For example, perhaps Thursday productivity is below the daily averages.

Predictive Analytics

Predictive analytics refers to the skills, technologies, applications and practices for exploration that answers questions:

● What if the trend continues? ● What will happen next? (prediction)

These can be generated continuously and/or iteratively, as in performing what if scenarios. Predictive analytics specifically applies inferential statistics, probability testing confidence intervals in the analysis.

Prescriptive Analytics

Iterative exploration on individual situations throughout a course of care, or of larger groups over time, answers the following questions:

● What actions are needed? ● What is the best that can happen?

For example, “Turning Hospital Data into Dollars,” in the February 2010 hfm magazine (see resource section), provides examples to answer these questions. One related to charge coding demonstrated the use of predictive modeling to indicate specific action to the undertaken, or not. The case “demonstrates the scale and comprehensiveness of predictive analytics. The model detected, with greater than 90% probability, 18 accounts as missing a charge for implantable joint device. In this example, the model analyzed all similar procedures for the previous 12 months and identified, with 93.6% probability, accounts that, if four conditions were performed in

conjunction, should have the artificial joint charge on the bill.” Review action was called for and taken in all cases. Others were left alone.

Prescriptive analytics iswhat should be done, as has been started. This may be based on predictive forecast with optimization rules applied to the prediction resulting in one or more courses of action prescribed. Predictions or inferential statistics need not necessarily be involved, however.

For example, to address central line infections, a set of known information, such as healthcare user initial condition, clinical care rules, and factors changing over time, such as healthcare condition, along with possible corollary effects based on time from the line’s placement, can be built into a rules engine to provide alerts on current and upcoming central line care and recommended changes. This work would likely be done by a Nurse Informaticist, if the role and function existed in one’s organization. If that role does not exist, get ready….this is your ball to carry.

What is the best that can happen? Not only is more beneficial care provided, the organization can understand potential workload more precisely to ensure adequate numbers of competent staff are available to perform this work and other work. Simplifications occurs as individual tracking of large variables sets is dramatically reduced and tracking is done on an exception basis. With or without a Nurse informaticist this an analytics task of the BI/Analytics practitioner.

Business Functional Areas of Focus

These are color coded as to whether they are typically possessed currently, are being developed, or recommended to be developed.

The lists are presented to encourage consideration of the myriad of possibilities.

Finance ● Financial statements analysis- ratios, as simple as cost per visit ● Financial ratios ● Statistics and Ration Medians. ● Labor compensation and supply cost ratios ● Key Performance Indicators ● Benchmarks-external/internal

Operations

● Operation management(core business processes): ○ Resource utilization design capacity versus effective capacity ○ On time first case in surgery-actual incision time to scheduled start ○ Benchmarks-external/internal ○ Processing flow/throughput/queues length or time ○ Capacity ○ Staff scheduling efficiency/effectiveness ○ Visit, procedure, etc. scheduling efficiency/effectiveness ○ Unit specific key performance indicators

Clinical ● Healthcare user satisfaction-HCAHPS ● Evidence based medicine pathways utilization/effectiveness ● 30-day readmission ● DRG/ICD/demographic groupings ● Risk management for HAIs, falls, decubitus ulcers, etc. ● Clinical specific key performance indicators ● benchmarks-external/internal