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Presentation1-2

What does mean information in Business Intelligence?

Information is a data that has related meaning within specific context.

What is the knowledge in Business Intelligence?

The knowledge is applying the information we took in use it in decision.

What is the wisdom?

Wisdom means using the knowledge for the greater good. It also requires a sense of bad and good, right and wrong, ethical and unethical.

Medical Definition of intelligence: Ability to perceive information, and retain it as knowledge to be applied towards adaptive behaviors within an environment.

Business Intelligence:  is technology a driven process for analyzing data and presenting actionable information to help end users( managers, patients etc..) take right decisions.

Paying for healthcare.

Important points:

  • Payments to the provider happen well after the services are actually rendered and an insurer is billed.
  • Providers pay the costs of rendering care at or before the time of service.

Major healthcare insurers:

  • Medicare – federal program for the elderly, disabled, & persons with ESRD (end-stage renal disease)
  • Medicaid – combination federal/state program covering persons with low incomes or disabilities
  • Commercial – usually available as a part of employment benefits, through some  available to individual.

 

Payment methods:

  • Retrospective versus Prospective: Retrospective means that the amount paid is determined by (or based on) what the provider charged or said it cost to provide the service after tests or services had been rendered to beneficiaries . However, In a prospective payment system (PPS), prices are set in advance and are known (or knowable) by all parties before care is provided.
  • DRGs which is diagnosis related to groups(patients) is a statistical system of classifying any inpatient stay into groups for the purposes of payment. The DRG classification system divides possible diagnoses into more than 20 major body systems and subdivides them into almost 500 groups for the purpose of Medicare reimbursement.
  • Ambulatory Payment Classification (outpatients)  are the government’s method of paying facilities for outpatient services for thje Medicare program
  • Per Diems/Case Rates : version of a prospective payment system is where the insurance provider pays for the patient’s healthcare based on the number of days the patient directly receives treatment from the health care provider
  • Capitation: a payment arrangement for health care service providers such as physicians or nurse practitioners. It pays a physician or group of physicians a set amount for each enrolled person assigned to them, per period of time, whether or not that person seeks care.
  • Fee for service:  is a payment model where services are unbundled and paid for separately. In health care, it gives an incentive for physicians to provide more treatments because payment is dependent on the quantity of care, rather than quality of care.

 

Health reform ( The Patient Protection and Accountable Care Act of 2010 (“PPACA”) )

Health care reform is a general rubric used for discussing major health policy creation or changes—for the most part, governmental policy that affects health care delivery in a given place.

Benefits of healthcare reform including:

  • Expanded health insurance coverage.
  • Reduced Medicare fees.

Profit Margins:

Profit Margins measures how much out of every dollar of sales a company actually keeps in earnings.

Key Financial Statement:

Income Statement:

details about revenues and expenses in particular period of time.  

Recommended Measures:

  • Operational Measures:  Volume ( such as average daily census ADC ..etc), Case&efficiancy ( such as patient satisfaction..etc), and staff ( such as staff turnover rate and salary expense per adjusted patient day…etc).
  • Clinical Measures: TJC ( such as Acute Myocardial Infarction AMI and Heart Failure core measure..etc), Patient stay ( such as Mortality rate and outlier ALOS..etc), and patient safety (  such as medication error rate… etc)
  • Financial Measures: Profit, liquidity, and capital&dept.

 

What is BI?

  • BI is the integration of data from disparate source systems to optimize business usage and understanding though a user – friendly interface.
  • Improving organizations by providing business insights to all employees leading to better, faster, more relevant decisions.

 

The reason of using BI.

  • BI helps organizations go from management by instinct to management by data.
  • providing your organization with key performance indicators that help manage revenue cycle management, quality and safety indicators, or outcomes associated with diabetes management, to name a few.

Important Points to be ATTENTION OF:

  • BI isn’t reporting, it isn’t analytics, it isn’t data warehousing, and it isn’t dashboards, but put them together and that is exactly what BI is.

C&B

Clinical & Business Intelligence (C&BI) is the use and analysis of data captured in the healthcare setting to directly inform decision-making.

C&B has the power to impact in many aspects including:

  •  Patient care delivery,
  • Health outcomes and
  • Business operations

leveraging the power of C&BI, we will see much improvement.

C&B ( HIMSS.ORG)

Clinical business intelligence is the use of data analysis to improve care delivery.

The Benefits of Business Intelligence:

  • Improve the management processes.
  • Improve operation processes.
  • Predict the future.

 Presentation #3 & #4

Leading a BI initiative:

  • Make sure that you spent time in strategy.
  • Get the right people in the right roles within the organization.
  • When possible, build consensus, but know when to step in and keep things moving
  •  Grow some thick skin—this isn’t a popularity contest.

Strategy and Execution:

The first time you do a strategic plan or road map you will have to invest more time in the following process:

  •  Create a working group.
  • Determine list of interviewees
  • Create an interview questions
  • Schedule interview.
  • Complete Interviews
  • Analyze data from interviews
  • Create categories of work
  • Visualize analysis in road map view.
  • Present to BI governance council.
  • Present to organization

Road map: strategy to execution.

Business opportunity ——-> Business Value ——> Business statement —- ) Requirements

Grow thick skin.

  • What is the important to me to paint a clear picture of what is like to lead  a BI program.

Why sponsorship is critical ?

  • Sponsorship implies support
  • Different from Leadership, sponsorship can be done by anyone in the organization
  • It’s ideal to have sponsors who are leaders (to ensure the longterm success of your program), but leaders shouldn’t be your only sponsors.
  • You will need to have sponsors from all levels.
  • Organizations change, people move around, others leave—but if you have support at each level of the organization, that’s your protection against the inevitable staff changes.

  Keeping your sponsor happy:

Happiness for sponsors of a BI program usually hinges on four factors:

  • Kudos received for all of the great things the BI team is doing for the organization.
  • High-quality data that supports good decision making for leadership.
  • Reasonable expectations
  • Return on investment

What is an executive sponsor ?

  • Just to be clear, this isn’t a checkbox activity.
  • Just having a sponsor isn’t enough.
  • Look for someone who has a lot to lose without BI.
  • You need a sponsor who is highly engaged in the project and will always be an advocate for your program when the boardroom door closes.
  • To maintain this support, you must keep your sponsor in the loop.

What is ROI ?

  • A financial measure to determine benefit to the organization
  • A form of cost-benefit analysis that measures the costs of a program versus the financial return from that program.

 

Step by step approach to calculate RIO

  • Return on Investment (ROI) is the ratio of money gained or lost on an investment relative to the amount of money invested
  • Cost benefit analysis (CBA) is a formal discipline used to help appraise or assess the case for a project or proposal, weighing the total expected costs against the total expected benefits..
  • Net Present Value : Net present value (NPV) is defined as the total present value of a time series of cash flows. It is a standard method for using the time value of money to appraise long-term projects.
  • Internal Rate of Return: The internal rate of return (IRR) is a capital budgeting metric used by organizations to decide whether they should make investments. It is an indicator of the efficiency or quality of an investment, as opposed to NPV, which indicates value or magnitude.

Step to calculate RIO

Step 1: Define the Business Goals of the Project.

Step 2: Measure the Goals.

Step 3: determine the investment.

Step 4: Calculate the ROI.

Step 5: Program Evaluation.

Lecture week 4:

Data quality definition:

Joseph Juran – “Data are of high quality if they are fit for their intended uses in operations, decision making and planning.”

Definition data governance:

Data governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed- upon models which describe who can take what actions with what information and when, under what circumstances, using what methods. (Thomas,2004)

Data Quality Implication for Healthcare:

  • The importance of good data quality cannot be underestimated; in a recent study on the value of data quality to organizations, it was found that even a 10 percent increase in the quality of data was attributable to $2 billion in revenue annually for a Fortune 1000 organization (Barua, 2011).
  • Transactional System
  • Many fields not mandatory due to workflow
  • Extract, transform, and load (ETL) practices can address these noisy data and make them into information that’s useful. Another data quality issue is the nature of healthcare data.

Data quality — Improves data quality

  • Organizations hesitate to implement data governance because they fear it will add an unnecessary level of red tape to BI projects.
  • Primary Function – give quality data
  • For healthcare, data governance is critical to regulatory compliance. A solid data governance function, with the appropriate amount of documentation, assists in meeting regulatory compliance and corresponding audits..

Definition data governance:

Data governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed- upon models which describe who can take what actions with what information and when, under what circumstances, using what methods. (Thomas,2004)

Primary layers:

 

Governance committee:

  1. Leaders
  2. Executives are preferable

Data stewards:

  • Integral to the long term success of BI
  • A job description needs to be defined depending on the industry.

 

Policies and procedures:

  • Document a mission statement for the governance function
  • Outline roles and responsibilities for the governance function
  • Provide an organizational chart of data governance
  • Determine minimum levels of participation
  • Document escalation procedures
  • Determine decision factors for high-level data standards
  • Establish decision rights of each governance layer

The concept of data binding:

Data can be “bound” to business rules that are implemented as algorithms, calculations, and inferences acting upon that data. Examples of binding data to business rules in healthcare include

◦ Calculating length of stay (LOS)

◦ Attributing a primary care provider to a particular patient with a chronic disease

◦ Calculating revenue (or expense) allocation and projections to a department or physician

◦ Data definitions of general disease states for patient registries

◦ Defining patient exclusion criteria for disease/population management

◦ Defining patient admission, discharge, and transfer rules.

Data can also be bound to vocabulary terms, for both local and industry standards. Examples of vocabulary binding include

◦ Patient identifier

◦ Provider identifier

◦ Location of service

◦ Gender

◦ Diagnosis code

◦ Procedure code

Knowing when and how tightly to bind data to rules and vocabularies is critical to the agility and success—or failure— of a data warehouse. In healthcare, the risks of binding data too tightly to rules or vocabularies are particularly high because of the volatility of change in the industry. Business rules and vocabulary standards in healthcare are among the most complex in any industry, and they undergo almost constant change.

The triple Aim of data governance :

  • Borrowing from the familiar IHI Triple Aim initiative for healthcare improvement, the Triple Aim of Data Governance is:

1- Ensuring data quality;

2- Building data literacy; and

3-Maximizing data exploitation for the organization’s benefit.

  • Ensuring data quality is the first step in a data governance mission.
  • Data quality is defined by the completeness of the data times its validity

( (Data Quality = Completeness x Validity). )

  • That is, collecting all the needed data for a particular analytic use case and ensuring that the data is valid.
  • The data governance committee and function must have strategies to support and improve data quality—ensuring completeness and validity of the data to support analytics.

________

 

  • The second aim is building data literacy throughout the organization, and the data governance committee should champion this initiative.
  • It makes no sense to build a library in an illiterate community.
  • Similarly, it makes no sense to invest in the technology and data content of an EDW in an organization that suffers from a lack of data literacy. (EDW = Enterprise Data Warehouse).
  • The data governance committee must sponsor training, education, and hiring practices that build the data literacy of the organization.

________

  • Finally, the third aim is data exploitation—maximizing the value of data to the organization, creating a data-driven culture that lowers costs, improves quality, and reduces risk.
  • It’s not enough to support data quality and data literacy.
  • Those attributes alone will not serve the betterment of the organization.
  • That data and those skills must be put to good use by creating a culture that constantly seeks self-improvement through the spotlight of data.

Mindset, Skillset, and Toolest:

  • Another useful three-part paradigm to guide the data governance committee is: mindset, skillset, and toolset, in that order of importance.
  • The data governance committee must play an active executive leadership role in the development of a data-driven mindset throughout the organization.
  • This is an important first initiative for the data governance committee— simply communicating from the executive level that the organization is, from this point forward, becoming a data-driven culture, constantly searching for ways to reduce their mean time to improvement.
  • The next step is the development of the skillset among the employees to support this data-driven mindset.
  • Finally, the data governance committee is the most logical choice for executive sponsorship of the toolset, such as an EDW, necessary to support the analytics journey.

Data Governance layers:

  1.  Executives & board leadership
  2. Data governance committee
  3. Data stewards
  4. Data architects & programmers.
  5. DBAs & system administrators.
  6. Data access control system
  7. EDW.

Healthcare analytic Models

Level 8 – Personalized  medicine & perspective analytic

Tailoring patient care based on population outcomes and genetics data. Fee-for quality reward health maintenance.

Level 7- clinical risk intervention & predictive analytics

Organizational processes for intervention are supported with predictive risk models. Fee-for quality include fixed per capita payment.

Level 6 – population health management & suggestive analytics.

Tailoring patient care based on population matrics fee-for-quality includes bundled per case payment.

Level 5- Waste & care variability reduction

Reducing variability in care processes. Focusing on internal optimization and waste reduction.

Level 4 – Automated external reporting.

Efficient, consistent production of report & adaptability to changing requirements.

Level3- Automated internal reporting.

Efficient, consistent production of reports & widespread availability in the organization.

level 2- standardized Vocabulary & Patients registers

Related and organizing the the core data content

Level 1 – Enterprise data Warehouse.

Collecting and integrating the core data content.

Level 0 – Fragmented points solutions

Inefficient. inconsistent versions of the truth. Cumbersome internal and external reporting.

Six  phases of data governance:

  1. Culture tone of data- driven.
  2. Access to data.
  3. Stewardship of data.
  4. Quality of data.
  5. Utilization of data.
  6. Acquisition of data.

You need to move through these phases in no more than two years.

Data profiling

  • Data profiling is a powerful tool for data quality.
  • It is our effort to lay claim to the data as it exists directly from the source systems.
  • This concept of data profiling allows you to understand the data at a detail level; specifically, you will ascertain how errorprone the data is.
  • Descriptive statistics, the mean, median, mode, range, minimum, and maximum, will provide a clear picture of how well the data behaves.

What is Data profiling and how can it help  with data quality:

  • Data Profiling is a systematic analysis of the content of a data source (Ralph Kimball).
  • You must look at the data; you can’t trust copybooks, data models, or source system experts
  • It is “systematic” in the sense that it’s thorough and looks in all the “nooks and crannies” of the data
  • You have to know your data before you can fix it

What type of analysis are performed:

  • Completeness Analysis

◦ How often is a given attribute populated, versus blank or null?

  • Uniqueness Analysis

◦ How many unique (distinct) values are found for a given attribute across all records? Are there duplicates? Should there be?

  • Values Distribution Analysis

◦ What is the distribution of records across different values for a given attribute?

  • Range Analysis

◦ What are the minimum, maximum, average and median values found for a given attribute?

  • Pattern Analysis

◦ What formats were found for a given attribute, and what is the distribution of records across these formats?

 What are some real world scenarios:

  • Data profiling can add value in a wide variety of situations. The basic litmus test is, “Is the quality of data important for this initiative?” If the answer is “yes”, then data profiling can help as it can quickly and thoroughly unveil the true content and structure of your data.
  • Some example scenarios include:

Data Warehousing / Business Intelligence (DW/BI) projects ◦ These projects involve gathering data from disparate systems for the purpose of reporting and analysis. Data profiling can help ensure project success by:

  • Identifying data quality issues that must be corrected in the source system
  • Identifying issues that can be corrected in ETL processing
  • Discovering unanticipated business rules
  • Even potentially providing a “no-go” decision on the project as a whole

Data profiling the Old way  

  • The “Manual” Approach
  • Traditionally, data profiling required a skilled technical resource who could manually query the data source using Structured Query Language (SQL). There is often a disconnect between the business analyst who knows what the data should be, and the technical programmer who knows SQL

Presentation 5

 What id BI

  • BI is the integration of data from disparate source systems to optimize business usage and understanding though a user – friendly interface.

Definition. Technology & Architecture.

  • A Business Definition Technology could cover anything from the iPhone that you have in your pocket to a server in your building.
  • Technology refers to the hardware and software associated with business intelligence (BI).
  • Similarly, architecture may be a new term for you in the context of IT. When you think about how most people understand architecture— blueprints for houses and remodels—that applies to our uses as well.
  • Simply, architecture for BI is planning out the use, allocation, and investment in technology (software and hardware) for application in a BI program.

Essential Clinical Analytics Technologies:

  • Clinical Analytics requires the thoughtful deployment of several business intelligence (BI) technologies.
  • It can transform diverse clinical data from multiple sources into meaningful information that can be used to take action to improve care.
  • The essential technical components are largely the same as for healthcare financial analytics and for business intelligence in other industries.
  • In fact, the most advanced healthcare organizations in deploying analytics to improve performance are moving towards unified platforms for financial and clinical analytics

Healthcare is difficult

  • Location

Where is your Data.

  • Data format

It is not digitized in healthcare.

  • Regulation & Requirements

Keeping up with the government

  • Data structure

Structure vs non-structured

  • Data definition

Subjective based on source

  • Data complexity

From human to data warehouse

Why is it difficult ?

  • Different data sources.
  • Different time horizons.
  • Different access devices.
  • Different levels of expertise
  • Politics
  • Risk
  • Different level of analytics need.

Four Stage Model;

The essential business intelligence technologies are aligned  around distinct stages:

  • Data acquisition
  • Integration
  • Enhancement
  • Information delivery

 

Data Acquisition

Data Acquisition involves getting relevant data elements out of source systems for transactional and operational functions such as:

◦ Clinical order entry

◦ Nursing documentation

◦ Medical records

◦ Surgical management

◦ Claims payment.

 

Data Acquisition

  • The classic approach to data acquisition is called Extract, Transform, Load (ETL).
  • ETL processes are typically run in batches on a periodic schedule. Depending on the purpose of the analysis, the updates may occur on any frequency from once a year to several times a day.
  •  In recent years, users have required more frequent updates so information can be analyzed and made available in ‘near real-time’.
  •  In some situations, health systems have moved beyond the batch ETL process altogether, using modern web service architectures that enable source systems to push individual transaction updates in real-time using XML document formats.
  • Similarly structured XML documents are also being used for data acquisition in multi-stakeholder environments such as Health Information Exchange (HIE).
  • Data acquisition requires technologies for fast, flexible tools for networking, for ETL and for messaging.

 

Data Integration

 

  • Data Integration involves structuring the data from multiple source systems into a unified data model that relates all data elements to one another in a meaningful way.
  • For clinical analytics, the main focus is usually on a longitudinal, person-centered data model that ties together different types of medical data about an individual, spanning multiple encounters with multiple providers over several years.
  • Designing a viable data model for complex healthcare information is one of the most challenging aspects of clinical analytics. Technical solutions, such a master patient index (MPI) for cleansing and standardizing data elements from different sources, are also important enablers of data integration.
  • Data dictionaries and related meta-data tools are also essential for a unified database to be efficiently accessed for analysis and reporting.
  • Enterprise solutions will typically integrate clinical data into an expansive data warehouse that includes information on a broad range of subject domains. For individual analytics applications, relevant subsets of the cleansed and organized data may be formed into smaller more manageable data marts.

 

Data Integration

Required tools for data integration include:

  • Modeling tools
  • Database engines
  • Fast large-scale data storage platforms

 

Data Enhancement

Data Enhancement involves adding value to the raw clinical data through classification schemes, risk adjustment formulas, and other processes that add new data elements that are useful in analysis.

 

Data Enhancement

Other forms of data enhancement involve statistical analysis of data in the aggregate rather than simply classifying an individual patient or encounter.  Specialized web service applications and flexible rulesengines are increasingly valuable technologies for implementing complex data enhancements.

 

Examples

  • Comparing the actual mortality rates for patients treated by different surgeons
  • Ranking those surgeons by their relative mortality rates
  • Assigning each physician to a performance quintile based on their mortality rates

 

Information Delivery

Information Delivery involves presenting information to a person who will use it to make a decision and take action.

The most basic forms of information delivery present a user with a report or chart that:

  • Shows trends over time in an important measure
  • Compares actual performance to an expected level
  • Compares the outcome of different providers or treatment

 

Dashboards and scorecards

  • Dashboards and scorecards similarly present trends or comparative performance information but use visual cues such as stoplight colors to focus the user’s attention on the most important issues.
  • Typically these basic reports, charts and dashboards are designed by an analyst or IT specialist and then delivered to users on a periodic schedule.
  • This traditional approach to ‘push’out information can work well for accountability reporting in a hierarchical organization.

 

Dashboards and scorecards

  • In clinical decision support, where doctors and nurses use information while caring for individual patients, different paradigms for information delivery are needed.
  • Example:
  • -A clinician making urgent care decisions does not have extra time to think about pulling data but needs the most relevant information pushed to them just at the right time without disrupting their workflow.
  • -information delivery for clinical decision support is ideally embedded in the software that the clinician is already using and presents guidance in form that makes it easy for the clinician to act on it. A pre-populated order screen is one example of information delivery for clinical decision support.
  • -As clinician make more use of mobile devices for managing patient care, delivering of analytic information will need to be embedded in mobile app as well.

Scaling up from data mart to data warehouse-data mart slide 22

The four-stage model for clinical business intelligence is relevant for all implementations regardless of size and scope.

  • A data mart is a small scale business intelligence solution for a single department or a single subject area. Even a narrowly focused data mart for a department such as a cardiology clinic will need to address all four stages of clinical analytics to get the data in, integrate it, enhance it, and deliver it to the end user.
  • Integration is often less of a challenge for departmental data marts since only a small number of sources systems are usually involved.
  • Stand-alone data mart scan be relatively quick to develop. A plethora
    of independent data marts in a large organization can be counterproductive because the separate data marts will provide inconsistent information, narrow data sets are not easily repurposed for new users and maintenance of multiple incompatible platforms become quite expensive.

Scaling up from data mart to data warehouse-data mart slide 23

  • An enterprise data warehouse (EDW) is a larger-scale solution that includes a wide variety of subject domains and serves a diverse group of users across many organizational departments and locations.
  • A unified EDW is very complex and takes substantial time to implement but the benefits include a ‘single source of truth’ for resolving question about organizational performance and the ability to leverage both information and technical expertise across numerous projects.
  • An EDW may feed its consistent, cleansed data into smaller data marts optimized to meet the needs of select groups of users.

Scaling up from data mart to data warehouse-data mart slide 23

Multi-stakeholder data warehouses are those that integrate data from several independent organizations. They share the same four-stage model but have some different challenges.

  • Standardizing data from multiple organizations can be very difficult. That is one reason that multi-stakeholder clinical business intelligence initiatives will often focuses on a narrower subject domain where the number of standardization and nomenclature issues can be limited. Patient registries focused on specific diseases or procedures are examples where narrow scope has contributed to success.
  • Many Health Information Exchange (HIEs) aim to eventually build clinical business intelligence solutions by combining participant information, but they must address issues of data ownership and privacy on their way to identifying specific projects with a strong enough ROI for all participants to justify the effort.

IT Infrastructure slide 25

  • Smaller data mart projects can sometimes be successful with standard hardware and database solutions like those used for transactional systems. Just using an SQL query to extract key data points and implementing a simple data visualization tool can sometimes be enough to yield useful insights. But implementing an enterprise-level or multi-stakeholder data warehouse requires a number of specialized IT tools tuned for business intelligence.
  • Healthcare organizations that traditionally looked to develop an enterprise analytics capability have needed to acquire numerous tools from several different vendors and take time to integrate the pieces into a useable platform. Key pieces of a classic IT infrastructure for analytics include:

Typical IT Infrastructure needs slide 26

Key pieces of a classic IT infrastructure for analytics include:

-High-speed network

-Data modeling tools

-computation servers

-Statistical packages

-storage platforms

-Data visualization tool

-ETL tools

-Reporting and dashboard tools

-Database engines

Healthcare-specific data warehouse slide 27

  • Healthcare-specific data warehouse products are available from a number of HIT vendors and include many of these technical components packaged into an application tailored for one or more aspects of performance analytics.
  • These solutions can be deployed relatively rapidly and can deliver solid ROI in the specific subject areas that their data models are designed to address. Larger healthcare organizations looking to develop a truly comprehensive enterprise warehouse may still need to select and deploy their own best-of-breed IT tools to create a unified database. Their EDW may in turn feed cleansed data into one or more focused analytics solutions from their HIT vendors.

Data Appliances slide 28

  • Data Appliances deliver an out-of-the-box analytics platform including all the hardware and software components needed for an optimized analytics platform.
  • Data appliance solutions are now available from a number of major IT vendors and provide new options for organizations deploying an EDW. Along with options for cloud-based deployment of analytics solutions, data appliances present an opportunity for healthcare organizations to speed up implementations of clinical analytics and reduce ongoing costs.

Staffing is top barrier slide 29

…but staffing is top barrier

In your organization, what are the top potential barrier to further diversifying the platform types in your data warehouse architecture?

29% lack of enterprise data architecture

34% poor quality of data

36% data integration complexity

38% lack of business sponsorship

43% data ownership and other politics

47% inadequate staffing or skills

Architecture for BI programs slide 30 

Scalability

  • Scalability is the ability of an application or product (hardware or software) to continue to function well when it is changed in size or volume in order to meet a user need (TechTarget, 2000).
  • Architecturally, there are three major areas where we can and should consider scalability:
  • The first is platform architecture. That is the hardware and software that we purchase to support our programs.
  • The second is enterprise application integration (EAI), which is the architecture that moves the data between the transactional systems and the data warehouse. This is where we can manage the reusability of the data through metadata, which will allow us improved ability to scale.
  • Finally, there is data architecture, the structure and relationships of the data created to support usage.
  • From a platform perspective the best way to do that is to consider how much activity the system will experience. We call this usage concurrency; in other words, how many people will be on the system at the same time doing similar activities.
  • Scaling in the context of data architecture is a bit more complex. There is a balance there of the functional things you can do with the data model, so it performs and the limitations of how you want to use the data. Conceptual models are drawings of how your data relates to one another and they should absolutely reflect the organizational needs of the data.
  • Finally, for scalability we discuss EAI. We discuss EAI in the context of usability as well, but for scalability we are most interested in the part of EAI that makes the data warehouse “source system agnostic.” What this means in business terms is that we can add or change any of our transactional systems (i.e., our electronic health record [EHR] or financial systems) and not impact our data warehouse. This is a big benefit for most data warehouse projects because in the lifetime of the data warehouse, a source system is likely to be changed.

Usability

  • Usability is the ease of use and learnability of a human-made object. The object of use can be a software application, website, book, tool, machine, process, or anything a human interacts with (Wikipedia, 2010).

Usability EAI

  • The process and platform provided by EAI allows us to improve user adoption by creating the baseline for metadata management.

Usability Security

  • Security should never be in just one spot. The more redundancy you have, the less likely you are to have an issue.
  • Security improves usability, not the other way around, because when people feel secure about the data they are more inclined to use it.

Usability Information architecture

  • Information architecture is much like it sounds; it is how we get from just data to information that improves business decisions. The way we do that in a BI system is to apply business rules. Business rules are the things that we know and understand about the data that makes the data more informative to our decision making.
  • Information architecture, or the method in which you apply these business rules is called extract, transform, and load (ETL).
  • ETL is like the special sauce that makes regular data into useful data.
  • ETL applies the business rules and supports much of our data quality efforts.
  • Good ETL supports your program goals.

Ten Best Practices for Healthcare ETL

  1. Apply a repeatable process
  2. Provide a strategic view
  3. Data profiling and data quality
  4. Utilize a data staging area
  5. Encourage reusable transformations
  6. Keep it timely
  7. Handling ETL errors gracefully
  8. ETL Testing
  9. Knowledge of ETL tools
  10. Compliance requirements

Repeatability

  • The definition for repeatability for our purposes is specific to process repeatability. It is the things that we do to ensure that we can repeat a process again with consistency and highquality deliverables.

Repeatability SDLC

  • Create and document repeatable processes
  • Often referred as SDLC
  • Which is BIDLC – BI Development Lifecycle
  • The activities must be done to pass through to the next stage; these are often referred to as stage-gates.
  • The BIDLC implies a strong connection to waterfall development. The waterfall methodology means that at the end of each stage there is a hand-off, often through documentation, to the next team.
  • Between analysis and design, you hand your business requirements to designers and walk away.

This continues through the life cycle from analysts to designer to developer to tester.

 

Presentation 7

Tenet 4: Value

  • Every aspect of healthcare BI must provide value
  • Healthcare organizations are overburdened and understaffed, particularly when it comes to data.
  • Focus where BI can provide the organization with the most value.
  • The value of BI comes from the delivery of reports, dashboards, and ad hoc analysis.
  • Deliver value based on users

Don’t boil the ocean

  • Do not boil the ocean.
  • Pick one project that would deliver value, create a solid foundation that provided room for growth, and focus all of your efforts on that first deliverable.

Training

  • To ensure value as perceived by end-users, be sure that you offer training.
  • Although it’s not usually the first thing that comes to mind when you start your BI program, it has the potential of making or breaking end-user adoption.
  • Today’s BI tools are easy to use compared to even five years ago, but that doesn’t mean that users will have an easy time adapting.

Business analytics

  • Business analytics, or business analysts, are an integral part of delivering value.
  • Business analysts are the people who document requirements from the business and translate them for consumption by IT.
  • They are not “analysts” in the sense of analyzing data.
  • The advancements in BI tools in the past few years have resulted in the ability to easily produce sophisticated data visualizations
  • From web to mobile, making sure that your data is easily consumed is a clear way to provide value.
  • They are the bridge between technical and business.

Devil is in details

  • Value, at the end of the day, is what our end-users perceive it to be.
  • If we make their lives easier by providing the right information at the right time in the right way, that is value.
  • The devil, of course, is in the details

BI team

  • Should be on the Business Side
  • All the technical aspects should be on the IT side
  • Business and IT should work hand-in-hand

BI DIRECTOR

  • Business intelligence is an interactive process for exploring and analyzing structured, domain-specific information (often stored in data warehouses) to discern business trends or patterns thereby deriving insights and drawing conclusions.
  • The business intelligence process includes communicating findings and effecting change across the organization. The director of business intelligence is responsible for creation and support of an enterprise business intelligence program and management of the data warehouse for business use.
  • This includes, but is not limited to, information governance, requirements gathering, report development, support of end-users, management of the data assets, generating demand for usage of data, and creating an effective communication and marketing plan for the enterprise data warehouse.

The trinity of user adoption

  • Training
  • Ease of use
  • Trust

The Trinity of User Adoption

  • Ease of Use and the Google Effect
  • User Interface
  • Improve Training to Reduce the Effect of Attention Scarcity
  • Training
  • What to do with the data
  • Managing Expectations to Build Trust
  • Build the trust of the user base from early on by tailoring your communications.

*Accessibility

*Reliability

*Consistency

*Honesty

The BI User Persona Continuum

  • Executive Persona
  • Analyst
  • Clinical
  • Associate

Six steps to providing value

  • Know your Users by Creating Personas
  • Meetings
  • Surveys
  • Workshops
  • Fixing your User Interface
  • Google
  • The point is, does the user interface (UI) respond to the user in the way in which the user expected it to respond? If it didn’t, the user is going to feel helpless and out of control.
  • Address Performance
  • Speed
  • Metadata is Mandatory
  • The main purpose of metadata is to facilitate in the discovery of relevant information, more often classified as resource discovery.
  • Metadata also helps organize electronic resources, provide digital identification, and helps support archiving and preservation of the resource.
  • Metadata assists in resource discovery by “allowing resources to be found by relevant criteria, identifying resources, bringing similar resources together, distinguishing dissimilar resources, and giving location information.”
  • Your Path to an Enlightened End-User community
  • If you don’t use it, you lose it.
  • Find a way to ensure that our systems are intuitive and easy to navigate.
  • Communicate , Understand and Listen
  • It’s of great consequence to ensure that every person in the organization knows about the BI program and can articulate the ways it brings value.
  • One way that is successful for BI programs is to plan how to market your program.
  • Communicate the value proposition of the program.
  • The best way to get started is to write a marketing plan – that will organize ideas and plan all critical steps.

Create a marketing plan for your program

A good marketing plan will have six main sections:

  1. Align with the mission statement of your organization.
  2. Write a powerful program objective.
  3. Create a communication plan.
  4. Identify the competitive landscape.
  5. Be creative with marketing activities.
  6. Build a project plan.

Align  with the mission statement of your organization

  • The BI program was created by the organization to deliver value back to the organization.
  • All of your objectives should be aligned from the highest level (mission statement) to the lowest level (data warehouse content).

Program Objective

  • Mission statement – Who, What and Why
  • Each Section will be more refined
  • “The BI Program will create a dashboard of the fourteen diseases that My Health System monitors. This dashboard will report the level to which each disease is under control based on evidence-based measurement parameters.”

Communication plan

  • Make sure that your communications are consistent and have an appropriate frequency.
  • Don’t underestimate how long communicating takes. Although it seems like it would take no time to write an email and send it, you need to ensure that the message is right on target.
  • Very smart people sometimes communicate to an audience in a way that completely loses the message.
  • Have your draft email proofread by someone in your corporate communications group or marketing team.
  • Know your audience, regardless of the content you are communicating. Remember the user continuum and your personas (executive, clinical, average associate, and analyst); you should apply the same rules to communicating to your audience as you do when thinking about training the end-user community.

comparative Landscape

  • Improving your competitive advantage is often a key deliverable of BI programs.
  • And even if it isn’t a deliverable, it’s still a good exercise to review what your competition does in its BI programs.

Marketing Activity

  • Understand the users
  • Market as per their needs
  • Training
  • Formal
  • Informal
  • Bulletins
  • Weekly meetings

Plan the work

  • Take all of the tasks and put them in a project plan.
  • Schedule them so they aren’t all happening at once, and allocate time to do them all (estimate the work and line up the resources).
  • Some should repeat and others will be a one-time communication.
  • Spread the work around. Even though the BI group is responsible for the communication, make sure you get as much help as reasonably possible.

New Innovations

  • BI is constantly changing. So you need to find a way to innovate all the time to ensure that the program stays relevant. Here are three ideas that provide more value with your existing program.
  • If you have access to a lot of data and you are used to just reporting what has happened, that’s an easy place to take it to the next level; ramp up your goal by trying to predict what will happen.
  • If you only have standard, boring spreadsheet-like reports, try creating visually appealing dashboards.
  • If you are looking for ways to get your user group more involved, extend them a challenge to find the most innovative ways to use the data and give a prize to the winner.
  • Work hard to ensure that you are always providing value

Evaluating the program For Value

  • Begin with the end in mind
  • What do you want to see when you are done?
  • How will you measure an improvement in user adoption?
  • What do your executives consider success?
  • How can you measure it?
  • It’s imperative to consider what value means to you and your leadership.
  • Reporting on those metrics requires that you gather the data to support the metrics.
  • Gathering the data can be done in a number of ways, including creating surveys that measure value, satisfaction, and data integrity or simply measure the number of users accessing the tools.
  • The latter is the least valuable way to measure success, because it doesn’t put the value of the usage into context, but it can be informative if you use it with other numbers (such as the total number of possible users).
  • Don’t settle for just putting numbers on a piece of paper. You are reporting the value of your program, so the numbers mean more than just the numeric value.
  • They represent value to the organization, satisfaction, ROI, and process improvement.
  • The context of the numbers is critical.
  • The number of current users of the system is meaningless, but the number of users as a percentage of all possible users is meaningful.
  • The number of reports run each quarter just means you are good at input/output; you have no idea if anything valuable was actually created as a result of those reports.
  • But the impact those reports had on decisions or customers speak to the core of your business.

Presentation 10

Four key trends of BI

  • Four key trends will impact the future of healthcare BI; in reality, some of them are impacting us today.
    • Integration of data from disparate sources (i.e., healthcare information exchanges).
    • The changing population of healthcare consumers is ushering in a whirlwind of change. This new population demands more information and is willing to seek it through social media and the interactive web (i.e., Web 2.0). This requires us to modify data privacy and security policies.
    • Mobile technologies for business intelligence.
    • Big data and analytics will drive home the value equation for healthcare BI.

Creating a Data-Driven Organization

In the short-term, organizations can:

  • Force decisions to be quantified.
  • Broaden your workforce data IQ.
  • Demonstrate data through visualizations.
  • Provide additional education.
  • Hire analytical people.

Better ETL

  • The future is now, and much of what is happening is on the Internet and in your organizations now.
  • Investing in extracting, storing, translating, and preparing your data for usage will be an investment in the long term for healthcare organizations.

Expectation of Future of BI

  • Mobile Healthcare
  • IoT & Cloud – Remote monitoring
  • Smart data – most of Unstructured data will be converging to structured data
  • Need for smart ETL
  • Demand for Real-time metrics – Clinical – Finance – Operations – anytime anywhere
  • Delivery could be visuals in hospitals, texts on mobile devices etc
  • Demand of DIY (Do It Yourself) BI Tools

BI – Understanding to Implementation  – to sum it All

  • Support – Leadership and Organization
  • Governance – Data, Structure
  • Choose right projects – that provide value
  • Understand required Technology and current Architectural gaps
  • Cultural preparedness
  • Marketing the program
  • Build Supporting Processes , Infrastructure and right tools
  • Train and Deploy
  • Operationalize the BI Function
  • Identify Key Performance Indicators (KPIs) for Healthcare

The strategic shift from management by instinct to management by data requires a commitment to business intelligence.