Cardiorespiratory Fitness, Health Outcomes, and Health Care Costs: The Case f...Firstbeat Technologies
Physical inactivity is becoming a world-wide epidemic – and the consequences can be both costly and deadly. This was outlined by Dr. Jonathan Myers who, citing a range of studies and recent research results, was able to show hard-hitting data related to the correlation between fitness (or lack thereof) and poor health. Myers argues fitness may well be a better marker than traditional risk factors for CVD and all-cause mortality. Amongst the eye-opening findings presented to the audience was that, for the first time, global deaths-per-year due to physical inactivity are higher than for smoking.
Mobile Health at Ochsner: The Apple HealthKit and Epic EMR IntegrationRahlyn Gossen
These slides are from the April 2, 2015 meeting of Health 2.0 New Orleans with special guest Jonathan Wilt, the Assistant Vice President of the Center for Innovation at Ochsner Health System. Jonathan spoke about Ochsner's Health System's integration of Apple HealthKit with the Epic EMR.
Audio is here: http://www.youtube.com/watch?v=UsSKui7m4VY
Cardiorespiratory Fitness, Health Outcomes, and Health Care Costs: The Case f...Firstbeat Technologies
Physical inactivity is becoming a world-wide epidemic – and the consequences can be both costly and deadly. This was outlined by Dr. Jonathan Myers who, citing a range of studies and recent research results, was able to show hard-hitting data related to the correlation between fitness (or lack thereof) and poor health. Myers argues fitness may well be a better marker than traditional risk factors for CVD and all-cause mortality. Amongst the eye-opening findings presented to the audience was that, for the first time, global deaths-per-year due to physical inactivity are higher than for smoking.
Mobile Health at Ochsner: The Apple HealthKit and Epic EMR IntegrationRahlyn Gossen
These slides are from the April 2, 2015 meeting of Health 2.0 New Orleans with special guest Jonathan Wilt, the Assistant Vice President of the Center for Innovation at Ochsner Health System. Jonathan spoke about Ochsner's Health System's integration of Apple HealthKit with the Epic EMR.
Audio is here: http://www.youtube.com/watch?v=UsSKui7m4VY
Holistic Forecasting of Onset of Diabetes through Data Mining Techniquesijcnes
Diabetes is one of the modern day diseases that poses serious threat for the affected and is ever challenging for physicians who are involved in its management and control.Type2 diabetes mellitus ranges in exponential rating day by day in its increase. Mere not being aware of the facts and causes that can lead to such state, unawareness about diabetic symptoms and late detection make diabetic condition unmanageable and is really a challenging task to be faced all victims. This paper suggests holistic measures and means by which any common man can get into it to check whether he / she is a would-be victim of Diabetes through simple checking of symptoms that may lead to Diabetic condition, analyses the factual causes of the aforesaid disease. This would certainly make a person to ensure for the locus-centric state of whether of being a diabetic or not. The problem of diagnosing the onset and incidence of Diabetes is addressed more with a data mining approach in mind. As the success of any data mining approach is solely dependant on the underlying dataset upon which learning is manifested and taken for, this paper inspects more on locating prima-facie symptoms of diabetes disorder. A sagacious insight of analyzing the actual causes of diabetes is set and hence a comprehensive set of data for diabetic condition is proposed here. Subjecting this data to data analysis through simple data mining techniques v.i.z., FP-Growth and Apriori would certainly model a holistic inference engine that could help a doctor to be more astute in confirming the diabetic condition of patients. Association rules are also being inducted based on both of these approaches. A heuristic computer aided diagnosis (CAD) system for diabetes can be built upon this
Aims -
* Diabetes – the big picture
* IoW – the context
* Self-management - a way forward
* Inspiring clinicians with what's possible, new patient pathways etc
* How we’ve turned things around, outcomes
Various Data Mining Techniques for Diabetes Prognosis: A Reviewijtsrd
Most of the food we eat is converted to glucose, or sugar which is used for energy. When you have diabetes, your body either doesnt make enough insulin or cannot use its own insulin as well as it should. This causes sugar to build up in your blood leading to complications like heart disease, stroke, neuropathy, poor circulation leading to loss of limbs, blindness, kidney failure, nerve damage, and death. Data mining adopts a series of pattern recognition technologies and statistical and mathematical techniques to discover the possible rules or relationships that govern the data in the databases. Data mining plays an important role in data prediction. There are different types of diseases predicted in data mining namely Hepatitis, Lung Cancer, Liver disorder, Breast cancer, Thyroid disease, Diabetes etc¦ This paper analyzes the Diabetes predictions. Misba Reyaz | Gagan Dhawan"Various Data Mining Techniques for Diabetes Prognosis: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12927.pdf http://www.ijtsrd.com/engineering/computer-engineering/12927/various-data-mining-techniques-for-diabetes-prognosis-a-review/misba-reyaz
Using Innovative Technology to Improve Medication Adherence. These slides were presented at Wessex Health Innovation Forum: Southampton on May 16th 2017.
EuroBioForum 2013 - Day 1 | Pierre MeulienEuroBioForum
EuroBioForum 2013 2nd Annual Conference
27-28 May 2013 - Hilton Munich City, Munich, Germany
http://www.eurobioforum.eu/2013
=======================================
# NATIONAL PERSPECTIVES #
Canada:
Genomics and personalised health in Canada
Dr Pierre Meulien, President and CEO at Genome Canada
=======================================
http://www.eurobioforum.eu
Holistic Forecasting of Onset of Diabetes through Data Mining Techniquesijcnes
Diabetes is one of the modern day diseases that poses serious threat for the affected and is ever challenging for physicians who are involved in its management and control.Type2 diabetes mellitus ranges in exponential rating day by day in its increase. Mere not being aware of the facts and causes that can lead to such state, unawareness about diabetic symptoms and late detection make diabetic condition unmanageable and is really a challenging task to be faced all victims. This paper suggests holistic measures and means by which any common man can get into it to check whether he / she is a would-be victim of Diabetes through simple checking of symptoms that may lead to Diabetic condition, analyses the factual causes of the aforesaid disease. This would certainly make a person to ensure for the locus-centric state of whether of being a diabetic or not. The problem of diagnosing the onset and incidence of Diabetes is addressed more with a data mining approach in mind. As the success of any data mining approach is solely dependant on the underlying dataset upon which learning is manifested and taken for, this paper inspects more on locating prima-facie symptoms of diabetes disorder. A sagacious insight of analyzing the actual causes of diabetes is set and hence a comprehensive set of data for diabetic condition is proposed here. Subjecting this data to data analysis through simple data mining techniques v.i.z., FP-Growth and Apriori would certainly model a holistic inference engine that could help a doctor to be more astute in confirming the diabetic condition of patients. Association rules are also being inducted based on both of these approaches. A heuristic computer aided diagnosis (CAD) system for diabetes can be built upon this
Aims -
* Diabetes – the big picture
* IoW – the context
* Self-management - a way forward
* Inspiring clinicians with what's possible, new patient pathways etc
* How we’ve turned things around, outcomes
Various Data Mining Techniques for Diabetes Prognosis: A Reviewijtsrd
Most of the food we eat is converted to glucose, or sugar which is used for energy. When you have diabetes, your body either doesnt make enough insulin or cannot use its own insulin as well as it should. This causes sugar to build up in your blood leading to complications like heart disease, stroke, neuropathy, poor circulation leading to loss of limbs, blindness, kidney failure, nerve damage, and death. Data mining adopts a series of pattern recognition technologies and statistical and mathematical techniques to discover the possible rules or relationships that govern the data in the databases. Data mining plays an important role in data prediction. There are different types of diseases predicted in data mining namely Hepatitis, Lung Cancer, Liver disorder, Breast cancer, Thyroid disease, Diabetes etc¦ This paper analyzes the Diabetes predictions. Misba Reyaz | Gagan Dhawan"Various Data Mining Techniques for Diabetes Prognosis: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12927.pdf http://www.ijtsrd.com/engineering/computer-engineering/12927/various-data-mining-techniques-for-diabetes-prognosis-a-review/misba-reyaz
Using Innovative Technology to Improve Medication Adherence. These slides were presented at Wessex Health Innovation Forum: Southampton on May 16th 2017.
EuroBioForum 2013 - Day 1 | Pierre MeulienEuroBioForum
EuroBioForum 2013 2nd Annual Conference
27-28 May 2013 - Hilton Munich City, Munich, Germany
http://www.eurobioforum.eu/2013
=======================================
# NATIONAL PERSPECTIVES #
Canada:
Genomics and personalised health in Canada
Dr Pierre Meulien, President and CEO at Genome Canada
=======================================
http://www.eurobioforum.eu
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
Triangles of Neck and Clinical Correlation by Dr. RIG.pptx
Bryn Mawr 2008
1. ITHW, Inc. Innovative Technologies in Health and Wellness David S. Lester, Ph.D. President, ITHW, Inc. Executive VP, Gene Express, Inc. October, 2008 A Systems Approach to Identifying Technology Interventions Based on Patient-Centered Outcomes
2.
3.
4.
5. The Key Is Identifying and Integrating Patient-Centric Technology Strategies to Prove Value to Multiple Stakeholders Payers Suppliers Employers Providers Patients Regulators Caregivers The Perception of Value Depends on Stakeholder Perspective Pharma ITHW, Inc. Innovative Technologies in Health and Wellness
6. The Healthcare System of Today: Which iPod do I trust? Regulators Pharmaceutical Companies Payers Providers Suppliers Caregivers Device Manufacturers Employers ITHW, Inc. Innovative Technologies in Health and Wellness
7. The Patient of Today – The iPod: What accessory do I choose? Eastern Medicines /Treatments Generics Pharmaceuticals Supplements Regulated Devices Non-Regulated Devices Nutraceuticals Physical Activities ITHW, Inc. Innovative Technologies in Health and Wellness
8. Developing Patient-Centric Technology Strategies Adding Value by Optimizing Key Points of Patient Impact Expanding Opportunities Across the Cycle of Patient Care Diagnosis Intervention Adherence Control Improved Outcomes Redefining & Identifying Diseases for Product Development Redefining Performance & Execution of Clinical Value Product Superiority/ Inferiority Novel Information Individualized Monitoring Pricing ITHW, Inc. Innovative Technologies in Health and Wellness
9.
10. Systems Dynamics Modeling of the Diabetic Patient Outcomes ITHW, Inc. Innovative Technologies in Health and Wellness
11. We are utilizing a proven method that enables us to integrate a wide range of dynamics and stakeholders Introduction to System Dynamics . . . ITHW, Inc. Innovative Technologies in Health and Wellness
12. MIT research shows that beyond three interacting feedback loops, intuition and conventional analysis break down Debt & Equity Passengers Flown Physical Capacity Service Capacity Product Attractiveness Shareholder Value Cause-effect relationships close in on themselves to form feedback loops – interacting feedback loops generate performance over time Introduction to System Dynamics . . . ITHW, Inc. Innovative Technologies in Health and Wellness ? ? Earnings Revenue (Unit Sales) Service Quality Customers Ability To Raise Capital Ability to Attract & Hire Employees
13. The complexity of diabetes and its C&Cs is reflected in their extensive interacting feedback loops Introduction to System Dynamics . . . ITHW, Inc. Innovative Technologies in Health and Wellness Atherosclerosis Obesity Stroke CHD Diabetes Depression Dyslipidemia Retinopathy Neuropathy Nephropathy Hypertension
14. Our second step was to organize diabetes and its complications and comorbidities into ten groups for the PoP effort Fasting Plasma Glucose (FPG) levels at presentation Type 2 Diabetes Diabetic FPG 126-299 mg/dL Non-diabetic FPG <100 mg/dL Pre-diabetic FPG 100-125 mg/dL Severe State Moderate State Non-state Pre-state Complication or comorbidity Specific classification index Obesity Coronary Heart Disease Stroke Atherosclerosis Dyslipidemia Hypertension Depression Nephropathy Neuropathy Adult 45-64 Adult 20-44 Adult 65+ Retinopathy When we distinguish three age groups, the number of groups triples from 120 potential patient pools to 360 potential patient pools Clinical landscape inventory . . . ITHW, Inc. Innovative Technologies in Health and Wellness
15.
16. Example of a complication of diabetes: Nephropathy Nephropathy * National Kidney Foundation/Kidney Disease Outcome Quality Initiative (NKF/KDOQI) classification system Complication/Comorbidity Clinical landscape inventory . . . Further details are provided in the supporting C&Cs Appendix ITHW, Inc. Innovative Technologies in Health and Wellness Demographics/Epidemiology Contributing factors Diagnostic Disease State Classification* Downstream Outcomes Sample Outcome Approximately 25-50 % of Type II DM patients will develop kidney disease, although do not present with symptoms until 5-10 years post onset of disease. Patients from an Asian or Afro-Caribbean origin are twice as likely to develop diabetic kidney disease. Diabetic nephrology accounts for approximately 40% of all cases of new end stage renal disease (ESRD). Hypertension, Atherosclerosis, Neuropathy. Severity of condition depends upon comorbidities of patient. Hyperglycemia and exposure to a high protein diet are important risks for development of proteinuria. Albumin (urine sample, first passing of day), creatinine (blood sample) Microalbumin-uria (marker of development of nephrology) -albumin levels over 30mg in 24h. Macroalbumin-uria (marker for progression to Stage V: ESRD)- albumin levels above 300mg. Serum creatinine levels outside normal range 0.8-1.3mg/dL indicates major kidney functional loss. Assessment of findings provide clues to stage of renal disease: Stage I: Time of diagnosis. Kidney size is increased. Glomerular Filtration Rate (GFR) is >90ml/min/1.73m 2 . Reversible by blood glucose control. Stage II: 2-3 yrs post-diagnosis. Glomerular basement membrane thickens and decline in renal function initiated. Scar tissue formation occurs. GFR 60-89ml/min/1.73m 2 Stage III: 7-15 yrs post-diagnosis. Microalbuminuria first appears. Glomerular damage has progressed and hypertension may be present. Patients are asymptomatic. GFR 30-59ml/min/1.73m 2 Stage IV: Overt, or dipstick positive, diabetes. Almost all patients have hypertension. Suboptimal glucose control. GFR 15-29ml/min/1.73m 2 Stage V: ESRD; GFR <15ml/min/1.73m 2 Renal replacement required. Coronary heart disease (due to macroalbu-minuria), Kidney failure
17.
18.
19.
20. Addition of technology impact points for all the diabetes populations and one C&C (obesity) expands the complexity OBESITY DIABETES Model structure . . . ITHW, Inc. Innovative Technologies in Health and Wellness NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality NQOL, e.g. “ bad days ” Non - intervention Non - diagnosis Non - adherence Noncontrol of condition Progression Mortality
21. The complexity is further enriched when the other C&Cs are added Model structure . . . ITHW, Inc. Innovative Technologies in Health and Wellness
22.
23.
24. When the simulation combines medical and epidemiological facts, interesting potential insights emerge For each impact point, the difference between the base case and an ideal situation (ie, no progress, no control problems, total adherence) was reduced by 50%. Here only the most influential half of impact points are shown. Technologies acting on which impact points would have the most influence*? Metric = Deaths averted compared to base case from 2008-2025 Not surprisingly, progression has the biggest impact on mortality. Due to the fact that even Pre-Diabeties significantly increases the risk of death in Older Adults, and that many pre diabetics become diabetics, preventing the onset of Pre-Diabeties has an even bigger impact than preventing the onset of full blown diabetes Keeping Non-diabetics from progressing to Pre-diabetics in general has more of an impact than keep Pre-diabetics from progressing to Diabetes because of the relatively larger Non-diabetic population, because Pre-diabetics exhibit some of the problems Diabetics Exhibit, and cutting down on Pre-diabetic development ultimately also decreases diabetes. Following Progression, Control and Adherence have the biggest impacts. While Progression of Non-diabetics to Diabetics has a larger impact than the progression of Pre-Diabetics to Diabetics, glucose control of diabetics has a larger impact than control of Pre-diabetics. Reference Point: 1 million deaths averted represents about 2% of the 50M total US deaths in the base case from 2008-2025. ILLUSTRATIVE RESULTS ONLY Model results . . . * “Progression, Non Diabetic, Older Adult” represents the progression of older, non-diabetic adults to older, pre-diabetic adults ITHW, Inc. Innovative Technologies in Health and Wellness
26. Preliminary categorization has identified several high-priority sub-groups for incorporation into the model e.g., Particular obesity treatments are not suitable for children e.g., Treatment efficacy might be influenced by sex hormones e.g., An anti-smoking treatment will not affect non-smokers * For additional information, see Appendix A: Supporting details for sub-group classification ** Priority ranking: 1 = highest 1 2 Considerations . . . ITHW, Inc. Innovative Technologies in Health and Wellness Smokers Age Gender Time with diabetes Alcohol consumers Ethnicity Genetic pre-disposition For how many C&Cs are the sub-groups relevant? 8 6 5 3 7 8 6 Is the prevalence of diabetes & its associated C&Cs altered among the different sub-groups of the classification? Yes Yes Yes Yes Yes Yes Yes Do differences exist in the Relative Risks (RRs) among the different sub-groups of the classification? Yes Yes Yes Yes Unclear Unclear No Suggested priority 1 1 1 1 2 2 3 Is a difference in technology impact among the different sub-groups of the classification likely to be observed? Yes Yes Yes Yes Unlikely No Unlikely Revised priority with consideration of the technology impact consideration 1 1 1 1 2 3 3
27. While expanding further patient sub-groups adds granularity to the model, the increased complexity must be managed Obesity Coronary Heart Disease Stroke Atherosclerosis Dyslipidemia Hypertension Depression Nephropathy Neuropathy Retinopathy Diabetic Non-diabetic Pre-diabetic Severe State Moderate State Non-state Pre-state Adult 45-64 Adult 20-44 Adult 65+ Adding only 2 further sub-group classifications across all C&Cs, each with 4 sub-groups, significantly increases complexity 4x3x3x10 = 360 “slices” in the model 4x3x3x10x(4x4) = 5760 “slices” in the model PoP (v1.0) Model PoP (v1.1) Model Considerations . . . ITHW, Inc. Innovative Technologies in Health and Wellness Obesity Coronary Heart Disease Stroke Atherosclerosis Dyslipidemia Hypertension Depression Nephropathy Neuropathy Retinopathy Diabetic Non-diabetic Pre-diabetic Severe State Moderate State Non-state Pre-state Adult 45-64 Adult 20-44 Adult 65+
28. Addition of Adherence Factors ITHW, Inc. Innovative Technologies in Health and Wellness
29.
30.
31.
32.
33. Total Population very closely matches historical estimates Younger Adults Middle Aged Adults Older Adults Simulation Historical Data Step #1: Historical Calibration… ITHW, Inc. Innovative Technologies in Health and Wellness Total Population by Age Group 1995.0 1996.8 1998.6 2000.4 2002.2 2004.0 20,000,000 40,000,000 60,000,000 80,000,000 100,000,000 120,000,000
34. THANK YOU Partners: PA Consulting Joe Alexander, Pfizer Human Health Technologies ITHW, Inc. Innovative Technologies in Health and Wellness
Editor's Notes
This presentation focuses on the key role human health technologies will play in determining leadership in the healthcare space. More importantly, it focuses on the vital role these technologies will play in enabling Pfizer to continue its leadership by developing new business models for delivering superior healthcare value.