This document outlines the functions of business intelligence (BI), including standard and ad hoc reporting, dashboards, machine learning metrics, Extract-Transform-Load (ETL) mapping, and user training. It discusses how BI can support operations and management, finance, payer utilization, care affordability, population health, and transformation through matching metrics to decisions. The document proposes leveraging reusable work products, services, and social technology to multiply analyst value through a feedback loop. It also discusses human factors that can amplify the technical model, including individual contribution, coaching, empowerment, and specific role transformations.

1. BI Functions
• Tie-verify report (QA)
• Ad hoc reports
• Cross-repository
queries
• Standard reports
• Dashboards/Scorecards
• Vendor COTS apps
• Machine learning
metrics
• Defs, pilots & modeling
• ETL Mapping
• Arch. & config.
• Source outliers, dups &
diferences
• DBA tuning & security
• BI portal & metadata
• User training
• Machine learning
Standardized,
leveraged, & scalable
BI processing
Operations & management
Finance & Productivity
• Pre-service, service
delivery, and post-service
billing & customer service
• Cost budgeting & finance
Payer utilization
• Shared savings attribution
• Bundled pymt contracts
• CAHPS experience
Care affordability
• Variation reduction
• Readmissions
• Generic Rx, IP to OP
• Total cost of care
• End of life UM
Population Health
• Risk prediction, groupers
• HEDIS & core measures
Transformation by
matching metric to
decision (model)
Formal
productivity
& logistics
method
• SDLC
• PMP
• UML
SysML
• Ad hoc
LOE
intake
Technology to analysis (operating model)
Business choice modeling
Translated into processing
algorithm, math equation, and/or
analysis criteria into a SMART
measurement model that
explains, clarifies & verifies.
• Event frequencies (OLAP root
cause drilldown)
• Pre/post change & differences
• Benchmarking specs (HFMA
MAP Keys, CAHPS, NCQA)
• Workflow cycle time
• Capacity constraint
• Allocation & optimization
• Patient queuing & scheduling
• Prognostics/prediction (rev. at
risk, likely denial cases)
• Conditional probability
• Markov chain
Integration w/
operational activities
that capture value
Workflow
handoff?
Formal
productivity &
logistics method
• Lean Six Sig
• Variation
Reduction
• Actuarial
utilization risk
• CMMI-SVC
• Effects
prediction
• Alemi rapid
decision
analysis
• CRSP-DM
Service
exchange?
Leveragere-useableworkproducttomultiplyvalue
Re-useable work products, services & social technology to multiply value
(feedback loop emphasizing
failing small & local)
Leveragere-useableworkproducttomultiplyvalue
What type of service handoff/exchange will energize, multiply, and amplify model capabilities
(chevron blue shapes) and the people who work in the environment?

2. Social technology (ways of organizing people to do things that amplifies their talents, such as use of
productivity improvement processes/engineering, definition of roles, measureable goals, multi-
disciplinary reflection, values, etc.)
Goal: Create resources that are re-useable (unsupervised use) to multiply analyst value, a
feedback/contribution by individual analysts of reusable knowledge to the org. to the point where a
decision modeler becomes replaceable. Reusable work products includes OLAP cubes, standard
reports, canned/template SQL for direct access, pseudo code business logic, annotated SQL code,
process maps/models, tables, rev. cycle rules, logical models, BI service specs, outlier exception
handling rule specs.
Future state: Frontline analyst’s choices and dept. management decisions are easy with just in time self-
service information. Outcomes/results are impacted by data modeled events/relationships of a given
problem. The core business choices that are modifiable are illuminated by simplified measurement
models (c.f., universe of all dimensions). Decisions are decomposed into component parts, and a
measurement model connects the computations.
What human factors will amplify a tech-to-
analysis operational model?
Individual contributor to cross-disciplinary partnerships
Lead by example
•Active listening &
gratitude
•Best analysis recognition
•Closing business decision
knowledge gaps
Coaching
•I do, we do, you do
•Failures/breakdowns to
new inspiring
commitments
•Milestone measurement
•Positive feedback
Support model
•Cross training internally &
across depts
•Change mgmt. training
•SME mentoring
•Job & goals alignment
Empowering futures
•Career skill development
action plans
•Supporting personal
commitments
•Innovation competition

3. • Analyst. Transforms error-filled raw data from siloed sources into information that is easily accessible
at the time business decisions and operational activity choices are made. Analysts scope/define
problems, as well as has the ability to compute and derive meaning/insights by comparing trends to
baselines, industry benchmarks, and tacit frontline prior knowledge. Can generate trends, workflow
timeliness metrics, detect errors, finds cost differences, determines root-causes, optimizes revenue
workflow, and finds modifiable gaps.
• Decision Modeler. Is a data/decision scientist that can scale computation of decisions with BI platform
tools so small tactical choices are easy and optimal. Constantly recombines multiple administrative and
clinical data elements to solve problems at the patient account/case or provider level, detect changing
events across functions, distinguish unwarranted variation, and can consult BI and business staff on
performance tuning.
Specific role transformation