Driving Decisions from
Predictive Modeling




Orlando, Florida




DATE : January 27, 2010
Introduction     2




Speaker Bios
         Anand Rao is a Partner at Diamond Management & Technology Consultants. Anand ...
Content   3




Content

  Market Context
  Our Predictive Modeling Thesis
  Strategic Applications: System Dynamics Model...
Market Context   4




Disruptive forces shaping Healthcare market
                      Rising Healthcare Costs        Co...
Market Context   5




Impact of Economic Recession
              Payers                                                Pr...
Market Context   6




Increasing Government Activism

                 Executive Order                Universal Health   ...
Market Context   7

Greatest force for change in next 5 years is
Healthcare Reform

  Reform is emerging in 2010.

  Major...
Market Context    8




Reform will further impact business models to change
                                             ...
Market Context   9

Different types of value are added at each step
in the clinical information chain
                    ...
Content   10




Content

  Market Context
  Our Predictive Modeling Thesis
  Strategic Applications: System Dynamics Mode...
Our Predictive Modeling Thesis   11




Diamond‟s Predictive Modeling Thesis

  Strategic and Operational predictive model...
Our Predictive Modeling Thesis   12




Strategic Vs. Operational Predictive Modeling Tools
Operational Decisions         ...
Our Predictive Modeling Thesis   13




Integrate Multiple Data Sources




        Which data would you look for to predi...
Our Predictive Modeling Thesis   14




Mature Statistical Techniques & Tools

  1880s: Linear Regression proposed by Galt...
Our Predictive Modeling Thesis   15




Good data visualization leads to smarter decisions

 Dr. John Snow‟s Visualization...
Our Predictive Modeling Thesis   16




40 Broad Street Water Pump




       Photo Credit: Miles Dowsett (www.milesdowset...
Our Predictive Modeling Thesis   17




Delivering predictions at the point of decision making
                           ...
Our Predictive Modeling Thesis   18




Delivering predictions at the point of decision making
                        E-P...
Our Predictive Modeling Thesis               19




Architecture is critical

                       Data
                ...
Our Predictive Modeling Thesis   20




Prototype, Pilot, Scale


                           Prototype                    ...
Content   21




Content

  Market Context
  Our Predictive Modeling Thesis
  Strategic Applications: System Dynamics Mode...
Strategic Applications   22




Strategic Vs. Operational Predictive Modeling Tools
Operational Decisions                 ...
Strategic Applications   23




Applicability of Simulation Techniques
High Abstraction
Less Details                 Aggre...
Strategic Applications   24




Application of Simulation Techniques in Healthcare

  Patient Flow Model
   – Within a Pro...
Strategic Applications   25




Patient Flow Model – Example
Strategic Applications   26




Patient Flow Model – Example
Strategic Applications   27




Two Case Studies
 Diabetes Management   Healthcare Policy (UK)
Strategic Applications    28




Compressed Morbidity: Longer life and fewer disabled years
               Life Expectancy...
Strategic Applications    29




Behind the Numbers: Compressed Morbidity

  Incidence of chronic disease increases with a...
Strategic Applications   30




Where we saw opportunity
                 Prototype Guiding Principles and Requirements
  ...
Strategic Applications   31




The Cost of Diabetes: The Big Picture
Strategic Applications   32




How we approached the opportunity
         Managed                                        ...
Strategic Applications   33

The SD Model as a candidate to help optimize Compressed
Morbidity
                Stocks, flo...
Strategic Applications   34




Applying the basics of SD to the Diabetes Opportunity
                                    ...
Strategic Applications   35




System Dynamics Economic Model Overview
    One of the major findings of the prototype was...
Strategic Applications   36

Digital Inclusion:
Can the right app really bridge the digital divide?
  One of the major fin...
Strategic Applications   37




Social Inclusion
 Attempts were made to broaden social circles and consequently make the
 ...
Strategic Applications   38




Technology Adoption Overview
     Patients showed a strong proclivity to adopt the Interne...
Strategic Applications   39




Service Health Findings
     Relationship between higher website usage and improved health...
Strategic Applications   40

Compliance is critical in managing Diabetes, here the pilot
excelled
     Mobile Devices were...
Strategic Applications   41




Sizing the Opportunity through SD Modeling
     Diabetes Complication Incidence with
     ...
Strategic Applications       42




Overview of Costs and Potential Benefits
  This prototype illustrates the economic ben...
Strategic Applications   43




Two Case Studies
 Diabetes Management   Healthcare Policy (UK)
Strategic Applications     44




Modeling „Coping‟ Policies in UK Healthcare Systems

  Model of UK Health and Social Car...
Strategic Applications     45




Patient Flow across Primary Care, Hospitals, and Social Care




        Source: Coping ...
Strategic Applications     46




Situation – Delayed Hospital Discharges Rising

                                        ...
Strategic Applications     47

Flow of medical inpatients and capacity structure of Hospitals
and Post-Hospital services
 ...
Strategic Applications     48




Medical Inpatient Model with four „Coping‟ Policies
   Formal policies were being „overr...
Strategic Applications     49




„Coping‟ Policies – Early Hospital Discharge

                                          ...
Strategic Applications     50

„Coping‟ Policies –
Overspill of Medical Patients to Surgical Beds

                       ...
Strategic Applications     51




„Coping‟ Policies – Service Referral Rate

                                             ...
Strategic Applications     52




„Coping‟ Policies – Insights

  Insights: In an attempt to suppress demand and accelerat...
Strategic Applications   53




Summary

 System Dynamics is an effective way of modeling healthcare policies at
 the
  – ...
Content   54




Content

  Market Context
  Our Predictive Modeling Thesis
  Strategic Applications: System Dynamics Mode...
Operational Applications   55




Strategic Vs. Operational Predictive Modeling Tools
Operational Decisions               ...
Operational Applications   56




DRIVE Platform : Accelerating Predictive Modeling Solutions

                       Diam...
Operational Applications   57




Example 1
      Pharmaceutical major trying to move away from a retail detail
      mode...
Operational Applications   58

Marketing Analytics Application Suite - Demonstration:
Physicians Map




        Source: D...
Operational Applications   59

Marketing Analytics Application Suite - Demonstration:
Output Dashboard




        Source:...
Operational Applications   60

Marketing Analytics Application Suite - Demonstration:
Socio-Demographic Charts




       ...
Operational Applications   61

Marketing Analytics Application Suite - Demonstration:
LifeStyle Behavior




        Sourc...
Operational Applications   62

Marketing Analytics Application Suite - Demonstration:
Health Risk Factors




        Sour...
Operational Applications   63

Marketing Analytics Application Suite - Demonstration:
Patient Persistence & Compliance



...
Operational Applications   64




Example 2
  More RFPs but limited underwriting bandwidth. Need for underwriters to
   fo...
Operational Applications   65




Input




        Source: DRIVE Architecture; Diamond Analysis
Operational Applications   66




Market Potential Analyzer




        Source: DRIVE Architecture; Diamond Analysis
Operational Applications   67




Profitability and Ease of capture




        Source: DRIVE Architecture; Diamond Analys...
Operational Applications   68




Compare Opportunities




       Source: DRIVE Architecture; Diamond Analysis
Content   69




Content

  Market Context
  Our Predictive Modeling Thesis
  Strategic Applications: System Dynamics Mode...
Behavioral Economics in Healthcare   70




Estimates for Behavioral Economics to reduce costs are varied
  The current "I...
Behavioral Economics in Healthcare    71

Simple behavioral interventions can influence what people eat
and how much they ...
Behavioral Economics in Healthcare   72

Diamond has used Agent Oriented Behavioral Modeling
on the Baby Boomer Segment
Di...
Behavioral Economics in Healthcare   73




Agent Oriented Behavioral Modeling
  The five segments are clearly differentia...
Content   74




Content

  Market Context
  Our Predictive Modeling Thesis
  Strategic Applications: System Dynamics Mode...
Summary   75




Summary

 Changing healthcare landscape
 Explosion of Information
 Increase in computing power
 Emergence...
Summary   76

Diamond Management & Technology Consultants
Papers and POVs
Q&A   77




Q&A

 Contacts
  – Anand Rao (anand.rao@diamondconsultants.com)
  – Richard Findlay (richard.findlay@diamondc...
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Driving Decisions From Predictive Modeling

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Driving Decisions From Predictive Modeling

  1. 1. Driving Decisions from Predictive Modeling Orlando, Florida DATE : January 27, 2010
  2. 2. Introduction 2 Speaker Bios Anand Rao is a Partner at Diamond Management & Technology Consultants. Anand has more than twenty years of experience in using advanced techniques, such as predictive modeling, agent-based simulation, and system dynamic techniques to analyze decision making situations. He has advised clients on a number of different aspects of customer experience and value management; behavioral economics and interventions; large scale transformation strategy, design, and execution. Anand is the lead proponent of the Belief-Desire-Intention agent modeling paradigm and was recognized for his contribution in this field with the Most Influential Paper Award for the decade by AAMAS in 2007. He has co-authored a number of papers, has written four books on building intelligent systems and is a frequent speaker at conferences on intelligent systems, behavioral economics, and advanced analytical techniques (anand.rao@diamondconsultants.com and www.anand-rao.com) Richard Findlay is a Practice Director in Diamond‟s Healthcare practice with over 25 years of experience across the healthcare industry. He has focused on developing for clients business strategies that create competitive advantage through the optimal use of Information Technology. His portfolio of expertise spans the value chain of operations with a particular focus on informatics, sales and marketing, supply chain, and clinical development. Within the industry Richard held senior executive positions with SmithKline Beecham and Abbott Laboratories. He is recognized as a leading authority on the future of healthcare and informatics frequently publishing and speaking at conferences on leading edge developments (richard.findlay@diamondconsultants.com) Amaresh has helped Fortune 500 companies in multiple industries to use bottoms-up data analytics in strategic decision making. His work has focused on developing growth strategies, defining market entry plans, understanding customer behavior to increase profitability, improve marketing efficiency, developing operations strategy and streamlining distribution. Amaresh founded Diamond‟s Information and Analytics practice and set up its delivery center in Mumbai, which he helps to manage. He is the editor of Diamond‟s information analytics blog (www.diamondinfoanalytics.com) and has written white papers on customer service, marketing segmentation and behavioral economics,. Amaresh holds a Masters degree in Transportation Systems Engineering from the University of Texas at Austin (amaresh.tripathy@diamondconsultants.com)
  3. 3. Content 3 Content Market Context Our Predictive Modeling Thesis Strategic Applications: System Dynamics Modeling & Demonstration Operational Applications: DRIVE Behavioral Economics in Healthcare Questions
  4. 4. Market Context 4 Disruptive forces shaping Healthcare market Rising Healthcare Costs Consumerism • Employer costs • Consumer Directed • Employee costs Health Plans • Sicker population • Transparency – Aging & Young • New technology • Fear of Change Technology Market Forces • Data collection & • Convergence of sharing value chain • Informatics & Healthcare Market • New business analysis models • Presentation & • Changing touchpoints industry • Decision support structure Economic Recession Government & planning • Healthcare reform • Employer bankruptcies and cost reduction • Focus on interoperability, • Payers losing group CPOE, and EHRs/ EMRs business • Fiscal stimulus • Reduced demand for providers • Consolidation
  5. 5. Market Context 5 Impact of Economic Recession Payers Providers • Stock market slide has hurt payer • Falloff in non-essential procedures reserves • Rising bad debt • Projected EPS growth ~5%, well • Focus on HMO operating model below 2004-07 gains (S&P) • Decline in group business Suppliers • Rx rates down • Looking to consolidate for scale • Restructuring to address new go to market strategies Consumers Employers • Growing numbers of unemployed • Carry most of healthcare cost and projected uninsured burden on top of other economic • Unfunded retirement burden challenges (Medicare and Social Security) • Pressure to reduce costs
  6. 6. Market Context 6 Increasing Government Activism Executive Order Universal Health HSA Improvement Stimulus and MMA mandates Care Choice and and Expansion Act Budget creates requirements Access Act to allow increases limits Packages. HSAs & for technology, use of pre-tax dollars and flexibility in HIT spend $20B. mandates transparency for individual health use and funding of Pharma Pricing eRecords and incentives premiums HSAs restraints 2003 Healthcare Legislation Timeline 2010 Tax-Free NIST will foster Healthcare Tax Relief and HIPAA the Savings, Access, Health Care establishes development of Protech Act and Portability Act increases EMEDS standards – a national in Act increases flexibility and Act compliance infrastructure Committee financial limits for by 2005 to share health attractiveness of funding HSAs data HSAs Source: Library of Congress – Thomas search.
  7. 7. Market Context 7 Greatest force for change in next 5 years is Healthcare Reform Reform is emerging in 2010. Major alignments will result in: a. Greater percentage of individual plans vs. group membership. b. Greater interaction with government data bases and programs for all value chain constituents. Predictive modeling will need to assess a. Legislation impact on member choices b. Legislation choices for non member options c. Impact and inference of “High Risk” pool d. Payers new product opportunities e. Payers need to re-segment the market place f. Specifics around impacts of closing the “doughnut hole” for payers & Pharma
  8. 8. Market Context 8 Reform will further impact business models to change Integrated „Holistic‟ Advice Integrated Hold Funds Alternative Investment Portfolio & Process & Financial Individual Platform and Manage Risk/ Capital Advice Benefits Transactions Planning Risk Mgmt Investments Markets Mgmt Back-office Risk Adviser Distributor Risk Aggregator Manufacturer Administrator Transferor
  9. 9. Market Context 9 Different types of value are added at each step in the clinical information chain Clinical Information Chain Informatics Presentation Decision Data & & Support & Collection Analytics Touchpoints Planning Collection, storage, Distilling large data Delivering the Driving behavioral aggregation and sets to guide information back to change to improve sharing from and to decisions for care and providers and patients health outcomes multiple sources business operations conveniently and coherently
  10. 10. Content 10 Content Market Context Our Predictive Modeling Thesis Strategic Applications: System Dynamics Modeling & Demonstration Operational Applications: DRIVE Behavioral Economics in Healthcare Questions
  11. 11. Our Predictive Modeling Thesis 11 Diamond‟s Predictive Modeling Thesis Strategic and Operational predictive modeling need different tools and analysis approaches Integration of multiple data sources, especially third party data, provides better predictions Statistical techniques are mature and normally not worth the incremental investment dollar Good data visualization leads to smarter decisions Delivering the prediction at the point of decision making is critical Architecture is critical Prototype, Pilot, Scale
  12. 12. Our Predictive Modeling Thesis 12 Strategic Vs. Operational Predictive Modeling Tools Operational Decisions Strategic Decisions Deterministic Learn drivers of Predict equilibrium point stock & flow over time Linear flows Feedback loops Point solutions Systemic understanding E.g., claims fraud, segmentation & E.g., Disease epidemiology, patient targeting, efficacy of disease flow models, impact of public policy management program reform Analytical Techniques Simulation Techniques Prediction e.g., Linear & Logistic Discrete-event Simulation Regression Agent Based Segmentation e.g., CHAID & Factor System Dynamics Analysis Dynamic Systems Optimization e.g., genetic algorithms and linear programming
  13. 13. Our Predictive Modeling Thesis 13 Integrate Multiple Data Sources Which data would you look for to predict dentist potential?
  14. 14. Our Predictive Modeling Thesis 14 Mature Statistical Techniques & Tools 1880s: Linear Regression proposed by Galton 1944: Logistic Regression proposed by Berkson 1954: Systems Dynamics developed by Forrester 1969: Backpropogration method in neural networks 1976: SAS Founded by Jim Goodnight 1993: R Open source statistical environment launched
  15. 15. Our Predictive Modeling Thesis 15 Good data visualization leads to smarter decisions Dr. John Snow‟s Visualization at 40 Broad Street (1854) Convinced city officials that cholera is a water borne disease
  16. 16. Our Predictive Modeling Thesis 16 40 Broad Street Water Pump Photo Credit: Miles Dowsett (www.milesdowsett.com)
  17. 17. Our Predictive Modeling Thesis 17 Delivering predictions at the point of decision making Order Forecasting at Grocery Store Area Sales Managers ASM can Override if he feels necessary FORECAST Inventory/ Forecast Hole Count Handheld Current Price Logic Application Start Over two days later Data Servers Data checks Next Day Delivery Place Order Reduction in OOS from 14% to 4%
  18. 18. Our Predictive Modeling Thesis 18 Delivering predictions at the point of decision making E-Prescription Rx RxHub Direct Connections Prescriber Health Plans 1. Select drug and connect to Payer to 2. Formulary/History brought to determine eligibility 3. Once Rx written, provider drug interactions are checked Rx Rx RxHub Prescriber Pharmacy 4. Send Rx to patient‟s pharmacy of 5. Renewal sent back to provider choice 3.3% Increase in prescription of generic drugs when using an e-prescription system with formulary decision support. Source: Archives of Internal Medicine Dec 2008.
  19. 19. Our Predictive Modeling Thesis 19 Architecture is critical Data Analytical Visualization & Reporting CRM, SFA, Mobile Device Integration Building Blocks Aggregation Engine Engine Engine Internal Data Predictive Mapping Lists & Scores External Data Modeling Graphing Pivots Syndicated Data Clustering & Survey data Segmentation Optimization Tableau Access SAS Tools MS Excel Oracle SPSS Mappoint MS SQL R Business Objects/Cognos Source: Archives of Internal Medicine Dec 2008.
  20. 20. Our Predictive Modeling Thesis 20 Prototype, Pilot, Scale Prototype Pilot Scale Define problem and Choose pilot area Update tactical hypotheses Pilot measurement elements based pilot Identify datasets framework learnings Develop model and Train and launch pilot Program integration Tasks output Gather feedback on points for scaling the Controlled pilot plan rollout process prototype Ongoing measurement plan 2 months 3 months 4 months Duration Source: Archives of Internal Medicine Dec 2008.
  21. 21. Content 21 Content Market Context Our Predictive Modeling Thesis Strategic Applications: System Dynamics Modeling & Demonstration Operational Applications: DRIVE Behavioral Economics in Healthcare Questions
  22. 22. Strategic Applications 22 Strategic Vs. Operational Predictive Modeling Tools Operational Decisions Strategic Decisions Deterministic Learn drivers of Predict equilibrium point stock & flow over time Linear flows Feedback loops Point solutions Systemic understanding E.g., claims fraud, segmentation & E.g., Disease epidemiology, patient targeting, efficacy of disease flow models, impact of public policy management program reform Analytical Techniques Simulation Techniques Prediction e.g., Linear & Logistic Discrete-event Simulation Regression Agent Based Segmentation e.g., CHAID & Factor System Dynamics Analysis Dynamic Systems Optimization e.g., genetic algorithms and linear programming
  23. 23. Strategic Applications 23 Applicability of Simulation Techniques High Abstraction Less Details Aggregates, Global Causal Dependencies, Feedback Dynamics,… Macro Level Strategic Level Agent Based System Dynamics (AB) (SD) • Active objects • Levels (aggregates) • Individual behavior • Stock-and-Flow diagrams rules • Feedback loops “Discrete Event” • Direct or indirect Middle Abstraction (DE) interaction Medium Details • Entities (passive • Environment models Meso Level objects) Tactical Level • Flowcharts and/or transport networks Dynamic Dynamics • Resources (DS) • Physical state variables • Block diagrams and/or algebraic-differential equations Low Abstraction Less Details Mainly discrete Mainly Continuous Micro Level Operational Level Individual objects, exact sized, distances, velocities, timings,… Source: From System Dynamics and Discrete Event to Practical Agent Based Modeling: Reasons, Techniques, Tools by Borshchev, A., and Filippov, A.
  24. 24. Strategic Applications 24 Application of Simulation Techniques in Healthcare Patient Flow Model – Within a Provider across multiple departments – Across primary, secondary, and community healthcare Disease epidemiology – Heart disease, Diabetes, HIV, cervical cancer, chlamydia infection – Dengue fever and drug-resistant pneumococcal infections Substance abuse epidemiology – Heroin addition, cocaine prevalence and tobacco reduction policy Healthcare capacity and delivery Interactions between public health capacity and disease epidemiology
  25. 25. Strategic Applications 25 Patient Flow Model – Example
  26. 26. Strategic Applications 26 Patient Flow Model – Example
  27. 27. Strategic Applications 27 Two Case Studies Diabetes Management Healthcare Policy (UK)
  28. 28. Strategic Applications 28 Compressed Morbidity: Longer life and fewer disabled years Life Expectancy at Age 85 9 Independent 8 Disabled 7 62% Remaining Years 6 5 47% 34% 4 3 72% 66% 2 53% 38% 35% 1 77% 0 1935 1965 1982 1999 2015 2022 Source: The Aging Boom: Demographic Trends and Policy Implications, Department of Elder Affairs State of Florida (H) Committee on Healthy Seniors, January 22, 2008 Charlie Crist Governor, State of Florida Statistics
  29. 29. Strategic Applications 29 Behind the Numbers: Compressed Morbidity Incidence of chronic disease increases with age, however; Improvements in disease management have reduced the disabling effects of morbidity. Therefore, even as there are increases in chronic disease there are reductions in disability at advanced ages; Leading to longer independent life-spans. Source: The Aging Boom: Demographic Trends and Policy Implications, Department of Elder Affairs State of Florida (H) Committee on Healthy Seniors, January 22, 2008 Charlie Crist Governor, State of Florida Statistics
  30. 30. Strategic Applications 30 Where we saw opportunity Prototype Guiding Principles and Requirements Patient Healthcare Intervention Spectrum Proposed Intervention Current Intervention Critical Intensive Awareness Prevention Occurrence Self-care Primary-Care Hospitalization Care Care Guiding Principles Prototype Requirements Meaningful and Measurable Multi-channel communication: Simple and Easy to Use – Mobile via SMS Social Networking – PC (Personal Or Computer @ Tele-Centre) Community Involvement 6 months of execution for gathering data Interactive Control group to evaluate efficiency & efficacy Participation Incentives
  31. 31. Strategic Applications 31 The Cost of Diabetes: The Big Picture
  32. 32. Strategic Applications 32 How we approached the opportunity Managed Gauteng Department of Health (GDoH) Prototype Overview Preventative 1 3 SMS Medication Reminder doctor/health : The Medical Care care worker records the frequency at which the SMS Location and Source of Educational Material : When educational material Using broadband as a patient should take medication. The system then becomes available, the system informs the platform, managing patients subsequently reminds the patient at the requisite patient via SMS about the nearest locations periodic intervals to ensure higher conformance where that material can be accessed. more effectively and to the medicine schedule. efficiently : Ecosystem Partners • Blue IQ (orchestrator) • GDoH (healthcare expertise) • Doctors, pharmacists, 2 4 patients (participants) SMS Nurse/Doctor Evaluation Reminder Collaborate via Social Networking The doctor/health care worker records the : Portal : Patient, Doctors, Nurses, etc frequency at which the patient should come for collaborate on a social networking • Content providers, ISPs, evaluation. The system then subsequently portal to share information, reminds the patient at appropriate intervals to concerns etc, and to post queries ensure higher conformance to the visit and answers. schedule. Impact Rationale Key Performance Indicators Key Performance Indicators • Better Citizen Health • Diabetes is one of the most costly CDL • Patient Knowledge • Improved Productivity • The direct and indirect economic impacts to citizens and • Capillary Blood Glucose Levels ( mmol/l) • Efficient and Effective governments can be very high • HbA 1C % Healthcare Service • 80% of diabetes can be well managed and easily controlled • Hospitalization rate Delivery • Body Mass Index
  33. 33. Strategic Applications 33 The SD Model as a candidate to help optimize Compressed Morbidity Stocks, flows and their causal relationships. Structure as interacting feedback loops Adoption Rate Potential Adopters Adopters + + B R Total Population + + Adoption from Adoption from Advertising B Word of Mouth + + + - Adoption Advertising Fraction Effectiveness Contact Rate Bass Diffusion model in VenSim
  34. 34. Strategic Applications 34 Applying the basics of SD to the Diabetes Opportunity Diagnosed Incidence & Managed Rate Adoption Un- Diagnosed Population diagnosed Managed Diagnosis Death Rate Rate Managed Managed Adoption Diagnosed Diabetes Un-managed Mortality Death Rate Un-managed
  35. 35. Strategic Applications 35 System Dynamics Economic Model Overview One of the major findings of the prototype was a clearer understanding of what it will cost to drive digital inclusion across Gauteng Macro view of model and the four major sections Population & Internet 1 Adoption • Inflow of diabetes patients 4 1 2 Diabetes patient lifecycle • Lifecycle from undiagnosed, through diagnosis ending in 2 mortality 3 Four management activities • Diet 3 • Exercise • Self Management • Clinic Management 4 4 Six major complications • Visual complications • Cardiovascular problems • Amputations • Neuropathy (Nerve) • Nephropathy (Kidneys) NOTE: Variables from Gauteng, South Africa and American diabetic sources combined with prototype-specific findings
  36. 36. Strategic Applications 36 Digital Inclusion: Can the right app really bridge the digital divide? One of the major findings of the prototype was a clearer understanding of what it will cost to drive digital inclusion across Gauteng Technology adoption shows some departure from the usual curve, with more people in incent and mandate category Costs ~ ZAR1,100 per person to adopt technology It should cost ~ ZAR1.1 Bn to make Gauteng fully digitally included
  37. 37. Strategic Applications 37 Social Inclusion Attempts were made to broaden social circles and consequently make the participant‟s worlds a bigger place. Over 60% of registered users were active in social media Source of Information No. of Respondents Interactive Media 52 patients mentioned online forum and blogs as important sources of Interactive information. Therefore, we do see that media Traditional people are expressing interest in being Media socially connected through ICT 201 patients mentioned Traditional mass Doctors & Traditional media (e.g. newspaper, TV, radio) as Nurses Media source of info for Diabetes 146 patients indicated that they still trust Friends & Doctors & doctors and nurses more and would go to Family Nurses them for any information 85 patients mentioned that they would Friends & reach their family members and friends Family for diabetes related info
  38. 38. Strategic Applications 38 Technology Adoption Overview Patients showed a strong proclivity to adopt the Internet as a means of education and information gathering Segmentation High usage of all three technologies i.e. internet, mobile, and Glucometer Moderate users of all three technologies Intervention users who didn‟t use Glucometer but used internet and mobile device Group who used Glucometer and mobile device Patients‟ response to simultaneous exposure to three different technologies – Internet, Mobile, Medical Device
  39. 39. Strategic Applications 39 Service Health Findings Relationship between higher website usage and improved health conditions/awareness
  40. 40. Strategic Applications 40 Compliance is critical in managing Diabetes, here the pilot excelled Mobile Devices were pivotal in increasing the hospital appointment compliance Snapshot Appointment Compliance Data for July and August
  41. 41. Strategic Applications 41 Sizing the Opportunity through SD Modeling Diabetes Complication Incidence with NO INTERVENTION* INTERVENTION IMPACT Complications Population Expense** ZAR Ketoacidosis 17 34,636 Visual 442,484 1,252,053,618 ~R800 million Amputations 14,202 89,303,542 Neuropathy 43,281 122,467,075 Estimated cost Cardiovascular 101,779 277,326,773 savings of Diabetes Nephropathy 7,252 20,520,972 TOTAL 609,015 1,761,706,616 hospitalizations for Gauteng: Diabetes Complication Incidence with BROADBAND ACCESS & SERVICES INTERVENTION* = Complications Population Expense** ZAR R524 Average Cost per Ketoacidosis 8 16,520 Inpatient Day 1 Visual 211,778 599,246,177 Amputations Neuropathy 6,813 20,501 42,839,276 58,010,938 x Cardiovascular 89,599 244,140,486 ~1.5M Hospital Days Nephropathy 6,384 18,065,331 Saved TOTAL 335,083 962,318,728 Note: (*)Based on 6 year modeled impact; (**)Expenses were calculated using a unique average length of stay for each complication Source: 1Estimating the Cost of District Hospital Services, Joseph Wamukuo & Pamela Ntutela
  42. 42. Strategic Applications 42 Overview of Costs and Potential Benefits This prototype illustrates the economic benefit per capita from just one service. The ~$90/citizen cost of adoption could be spread over multiple services to maximize the benefit. ~$90/citizen to deliver ~$230/citizen of and have services realized benefit executed - = ~$140/citizen for just one critical service* * It is highly likely that one citizen will realize benefit from multiple services Source: ICT Enabled Preventive Intervention, Diamond Consultants
  43. 43. Strategic Applications 43 Two Case Studies Diabetes Management Healthcare Policy (UK)
  44. 44. Strategic Applications 44 Modeling „Coping‟ Policies in UK Healthcare Systems Model of UK Health and Social Care – NHS, Primary Care Trusts, Local Government Social Services Directorates System dynamics model of a typical health community covering the whole patient pathway from primary care, through hospitals and onward to post-hospital services Incentives and penalties in one part of the chain can lead to „coping‟ policies that can be counter-productive Based on work carried out by Eric Wolstenholme and others in UK (1999-2007) Source: Coping but not coping in health and social care: masking the reality of running organizations beyond safe design capacity; Wolstenholme et.al. Vol 3. No. 4, System Dynamics Review, 2007
  45. 45. Strategic Applications 45 Patient Flow across Primary Care, Hospitals, and Social Care Source: Coping but not coping in health and social care: masking the reality of running organizations beyond safe design capacity; Wolstenholme et.al. Vol 3. No. 4, System Dynamics Review, 2007
  46. 46. Strategic Applications 46 Situation – Delayed Hospital Discharges Rising Delayed hospital Delayed Hospital Discharges discharges started rising rapidly The government felt that Social Services could do much better at assessing and placing older people in post-hospital services Fined Social Services for delayed discharges Problem started getting worse – Why? Source: Coping but not coping in health and social care: masking the reality of running organizations beyond safe design capacity; Wolstenholme et.al. Vol 3. No. 4, System Dynamics Review, 2007
  47. 47. Strategic Applications 47 Flow of medical inpatients and capacity structure of Hospitals and Post-Hospital services When this structure was simulated over 3 years the results showed significant accumulations in the “medical treatment backlog” and “waiting discharge to post-hospital services” states, over those observed in practice – even though they were not allowed in practice. Source: Coping but not coping in health and social care: masking the reality of running organizations beyond safe design capacity; Wolstenholme et.al. Vol 3. No. 4, System Dynamics Review, 2007
  48. 48. Strategic Applications 48 Medical Inpatient Model with four „Coping‟ Policies Formal policies were being „overridden‟ by informal policies that had an adverse impact on the overall flow of patients through the system Source: Coping but not coping in health and social care: masking the reality of running organizations beyond safe design capacity; Wolstenholme et.al. Vol 3. No. 4, System Dynamics Review, 2007
  49. 49. Strategic Applications 49 „Coping‟ Policies – Early Hospital Discharge Informal Policy: Length of stay in hospital for normal cases became a managerial policy variable, rather than a constant based on patient need and condition Positive Impact: Early discharge of normal patients is an effective option for hospitals to reduce their medical treatment backlog Negative Impact: Reduced length of stays in hospital create incomplete episodes of care and this can result in increases in the percentage of readmissions. Source: Coping but not coping in health and social care: masking the reality of running organizations beyond safe design capacity; Wolstenholme et.al. Vol 3. No. 4, System Dynamics Review, 2007
  50. 50. Strategic Applications 50 „Coping‟ Policies – Overspill of Medical Patients to Surgical Beds Informal Policy: Transfer of medical patients to surgical beds whenever referrals exceeded bed capacity Positive Impact: Reduces immediate medical treatment backlog Negative Impact: Medical patients occupying surgical beds result in cancellation of surgical procedures and increase in elective surgical wait times Conditions of patients waiting will deteriorate and cause medical emergencies, and push the medical treatment backlog Source: Coping but not coping in health and social care: masking the reality of running organizations beyond safe design capacity; Wolstenholme et.al. Vol 3. No. 4, System Dynamics Review, 2007
  51. 51. Strategic Applications 51 „Coping‟ Policies – Service Referral Rate Informal Policy: With excessive waiting for medical admission to hospital, the referral threshold was changed to reduce referrals Positive Impact: Reduced immediate medical treatment backlog Negative Impact: Pushes demand further back upstream and ultimately this has to be absorbed by stocks outside the health and social care system Source: Coping but not coping in health and social care: masking the reality of running organizations beyond safe design capacity; Wolstenholme et.al. Vol 3. No. 4, System Dynamics Review, 2007
  52. 52. Strategic Applications 52 „Coping‟ Policies – Insights Insights: In an attempt to suppress demand and accelerate throughput, coping mechanisms (fixes) are put into place that may do more harm than good, by impacting people (inside and outside of the organization‟s boundaries) in such a way that they do not get the care they need, although the organizations existing metrics might not tell you that Solution: Increasing the care package capacity within social services was not only shown to be a cheaper solution than increasing hospital capacity, but was demonstrated to be a win–win situation for both health and social services Source: Coping but not coping in health and social care: masking the reality of running organizations beyond safe design capacity; Wolstenholme et.al. Vol 3. No. 4, System Dynamics Review, 2007
  53. 53. Strategic Applications 53 Summary System Dynamics is an effective way of modeling healthcare policies at the – Patient level – HMO, PPO, POS level – National levels It can model formal and informal policies and behaviors of all stakeholders Effective way of combining statistical data and qualitative information Simulate behaviors and delayed feedbacks over time
  54. 54. Content 54 Content Market Context Our Predictive Modeling Thesis Strategic Applications: System Dynamics Modeling & Demonstration Operational Applications: DRIVE Behavioral Economics in Healthcare Questions
  55. 55. Operational Applications 55 Strategic Vs. Operational Predictive Modeling Tools Operational Decisions Strategic Decisions Deterministic Learn drivers of Predict equilibrium point stock & flow over time Linear flows Feedback loops Point solutions Systemic understanding E.g., claims fraud, segmentation & E.g., Disease epidemiology, patient targeting, efficacy of disease flow models, impact of public policy management program reform Analytical Techniques Simulation Techniques Prediction e.g., Linear & Logistic Discrete-event Simulation Regression Agent Based Segmentation e.g., CHAID & Factor System Dynamics Analysis Dynamic Systems Optimization e.g., genetic algorithms and linear programming
  56. 56. Operational Applications 56 DRIVE Platform : Accelerating Predictive Modeling Solutions Diamond‟s DRIVE Platform Visualization & Data Aggregation Analytics Reporting Internal Data Predictive Modeling Graphing External Data Clustering & Segmentation Mapping Syndicated Data Optimization Lists & Scores Survey data Pivots Best of breed technology infrastructure Complements Diamond‟s management consulting practice Helps clients develop and test predictive modeling prototypes rapidly
  57. 57. Operational Applications 57 Example 1 Pharmaceutical major trying to move away from a retail detail model to a more consultative model for marketing to physicians (also relevant for payors and PBMs) Identify patient population & physician group to target prescription drug compliance and adherence program Predict patient population and/or physicians who have patients who are
  58. 58. Operational Applications 58 Marketing Analytics Application Suite - Demonstration: Physicians Map Source: DRIVE Demonstration; Diamond Analysis
  59. 59. Operational Applications 59 Marketing Analytics Application Suite - Demonstration: Output Dashboard Source: DRIVE Demonstration; Diamond Analysis
  60. 60. Operational Applications 60 Marketing Analytics Application Suite - Demonstration: Socio-Demographic Charts Source: DRIVE Demonstration; Diamond Analysis
  61. 61. Operational Applications 61 Marketing Analytics Application Suite - Demonstration: LifeStyle Behavior Source: DRIVE Demonstration; Diamond Analysis
  62. 62. Operational Applications 62 Marketing Analytics Application Suite - Demonstration: Health Risk Factors Source: DRIVE Demonstration; Diamond Analysis
  63. 63. Operational Applications 63 Marketing Analytics Application Suite - Demonstration: Patient Persistence & Compliance Source: DRIVE Demonstration; Diamond Analysis
  64. 64. Operational Applications 64 Example 2 More RFPs but limited underwriting bandwidth. Need for underwriters to focus on accounts with maximum likelihood to win and most profitable Predict where to deploy the underwriting resources in the small business segment of a payer Identity opportunity and attractiveness of prospective clients and markets
  65. 65. Operational Applications 65 Input Source: DRIVE Architecture; Diamond Analysis
  66. 66. Operational Applications 66 Market Potential Analyzer Source: DRIVE Architecture; Diamond Analysis
  67. 67. Operational Applications 67 Profitability and Ease of capture Source: DRIVE Architecture; Diamond Analysis
  68. 68. Operational Applications 68 Compare Opportunities Source: DRIVE Architecture; Diamond Analysis
  69. 69. Content 69 Content Market Context Our Predictive Modeling Thesis Strategic Applications: System Dynamics Modeling & Demonstration Operational Applications: DRIVE Behavioral Economics in Healthcare Questions
  70. 70. Behavioral Economics in Healthcare 70 Estimates for Behavioral Economics to reduce costs are varied The current "Information Overload and Accessibility" is resulting in abdication from decisions to change for both: – Patients – Providers Behavioral Economic structured interventions in the information based decision tree can yield positive results Many initiatives on individual therapeutic classes have demonstrated success – Diabetes – Asthma – Smoking At Diamond we are in the process of refining a total HC model where initial cost reductions from such programs can yield savings in the $1 to 2 billion range nationally
  71. 71. Behavioral Economics in Healthcare 71 Simple behavioral interventions can influence what people eat and how much they eat OBESITY BE Interventions 1. Placing candies three feet away from one‟s 31% desk reduced volume of chocolate consumption by 5 to 6 chocolates a day (Self-control) 15% 2. Subjects provided with a bowl of M&Ms in 10 colors ate 77% more than people given a bowl with only 7 colors (Visceral effects) 3. Food stamp benefits raise food expenditure <20 yrs 20-74 yrs more than an equal amount in cash (Mental Accounting) 1. Obesity causes at least 300,000 excess deaths 4. Pre-ordered healthy-pack options encouraged healthy eating by Food Stamp Beneficiaries in 2. Obesity in adults resulted in health care Connecticut and North Carolina (Defaults) costs of $93 billion in 2002 5. Having more unhealthy choices reduces the 3. Lifetime costs related to diabetes, heart chances of health options being selected – disease, high cholesterol, hypertension Salad, Hamburger, Cake vs Salad and and stroke among obese are $10,000 Hamburger (Choice Relativity) more than the non-obese Source: Could Behavioral Economics Research help improve Diet Quality for Nutrition Assistance Program participants, USDA, Economic Service, Diamond Analysis
  72. 72. Behavioral Economics in Healthcare 72 Diamond has used Agent Oriented Behavioral Modeling on the Baby Boomer Segment Diamond's market research on baby boomer health and wealth attitudes and behaviors identified five significant clusters of consumers Low Financial Confidence High Financial Confidence High Health Consciousness Aspirants High Health Consciousness Moderates 31% 24% (56yrs/ (62yrs/ 20% $50K) $98K) (57yrs/ Affluent Percent of $31K) population Sophisticates Avg. Age/ 15% Avg. Income (66yrs/ $50K) Retired Settlers Survivors 10% Low Financial Confidence (57yrs/ High Financial Confidence $24K) Low Health Consciousness Low Health Consciousness Source: Diamond Retirement Study, 2008
  73. 73. Behavioral Economics in Healthcare 73 Agent Oriented Behavioral Modeling The five segments are clearly differentiated in terms of their health consciousness (e.g., regular exercise, health insurance cover, health risk during retirement) Increasing Health Consciousness Affluent Survivors Moderates Aspirants Retired Settlers % who exercise at least 3 hours a week Sophisticates 49% 27% 29% 30% 15% 60% 84% % who strongly agree that they have adequate health insurance 50% 17% 23% % who ranked physical health as most at risk during retirement 63% 71% 42% 39% 26% Source: Diamond Retirement Study, 2008
  74. 74. Content 74 Content Market Context Our Predictive Modeling Thesis Strategic Applications: System Dynamics Modeling & Demonstration Operational Applications: DRIVE Behavioral Economics in Healthcare Questions
  75. 75. Summary 75 Summary Changing healthcare landscape Explosion of Information Increase in computing power Emergence of sophisticated tools and techniques Opportunity to design and model new marketing and behavioral interventions in healthcare – DRIVE in Pharmaceuticals and Payer – System Dynamics in Diabetes Intervention and Policy Formulation – Behavioral Economics in healthcare
  76. 76. Summary 76 Diamond Management & Technology Consultants Papers and POVs
  77. 77. Q&A 77 Q&A Contacts – Anand Rao (anand.rao@diamondconsultants.com) – Richard Findlay (richard.findlay@diamondconsultants.com) – Amaresh Tripathy (amaresh.tripathy@diamondconsultants.com)

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