Digital transformation in healthcare: risks and opportunities Peter Klimek Singapore, July 18 2017
Our health care systems are unsustainable. Singapore Main challenge: noncommunicable diseases
Noncommunicable diseases Leading cause of death (83% in Singapore, 68% world-wide, from those more than 40% premature) 1 out of 4 Singaporeans aged above 65y developed a chronic disease in 2015 (Singapore Life Panel) How to respond to this challenge
Will new cures solve this problem? Remarkable advances for some specific diseases (certain types of cancers) ...
Will new cures solve this problem? ... but not so much for many chronic disorders where lifestyle & environment are Rate of productivity of developing new drugs is going down important risk (USD 5billion per drug) factors Rate of failure in clinical trials goes up (attrition rate of 95%)
Will healthy ageing initiatives solve this problem? Europe USA Singapore WHO
There is no Healthy Ageing? Europe USA healthy ageing in this sense. Singapore Instead, we need a new vision for medicine WHO
Complexity Perspective • Chronic diseases have many different risk factors (genetic, social, environmental, ... networks) • Example Type 2 Diabetes: ~30 genes known that explain only about 10% of disease Social networks occurrences • Each risk factor might be involved in several Metabolic pathways disorders patients have multiple disorders Gene-regulatory networks • Need to understand networks involved in disease and how they influence each other to repair them and prevent disorders Protein-protein interactions
What is medicine? Vision managing networks = preventing & curing diseases
Big Data – a gamechanger? • If you have disease A, how likely are you to have another disease B? • Disease-disease networks of 141,000 classified diseases Phenotypic / comorbidity data • 1,000 known drugs • Disease-drugs networks • Re-use of population-level health care (claims) data (safeguarded by HVB) • Anonymized research dataset containing each medical treatment and its cost in Austria Health-care data over two years • 100,000,000 doctor visits and 2,000,000 hospitalizations per year and health-care provider networks with 8,000,000 patients and 12,000 health care provider • Social networks and computational social science Social, environmental, • Geo-localized data on exposure to risk factors (air-particulates, density of fast food demographic data restaurants, ...) • Demographic trends and population structure • Human Genome Project (25,000 genes) • Gene regulatory networks (transcription, replication, translation, ...) Molecular / physiological data • Protein-protein interaction networks (100,000 proteins) • 1,000 metabolic pathways and their networks
Big Data – a gamechanger? • If you have disease A, how likely are you to have another disease B? • Disease-disease networks of 141,000 classified diseases Phenotypic / comorbidity data • 1,000 known drugs • Disease-drugs networks • Re-use of population-level health care (claims) data (safeguarded by HVB) • Anonymized research dataset containing each medical treatment and its cost in Austria Health-care data over two years • 100,000,000 doctor visits and 2,000,000 hospitalizations per year and health-care provider networks with 8,000,000 patients and 12,000 health care provider • Social networks and computational social science Social, environmental, • Geo-localized data on exposure to risk factors (air-particulates, density of fast food demographic data restaurants, ...) • Demographic trends and population structure • Human Genome Project (25,000 genes) • Gene regulatory networks (transcription, replication, translation, ...) Molecular / physiological data • Protein-protein interaction networks (100,000 proteins) • 1,000 metabolic pathways and their networks
Big Know How Data
Understanding disease networks patients with pancreatic cancer patients with diabetes diabetes pancreatic cancer • nodes = diseases • links = diseases are often co- occurring • size of nodes = disease prevalence patients with diabetes and pancreatic cancer
Understanding disease networks 0-8 years 9-16 years 17-24 years 25-32 years 33-40 years 41-48 years 49-56 years 57-64 years 65-72 years
Understanding disease networks 0-8 years 9-16 years 17-24 years 25-32 years 33-40 years 41-48 years 49-56 years 57-64 years 65-72 years
Understanding disease networks 0-8 years 9-16 years 17-24 years 25-32 years 33-40 years 41-48 years 49-56 years 57-64 years 65-72 years
Understanding disease networks 0-8 years 9-16 years 17-24 years 25-32 years 33-40 years 41-48 years 49-56 years 57-64 years 65-72 years
Understanding disease networks 0-8 years 9-16 years 17-24 years 25-32 years 33-40 years 41-48 years 49-56 years 57-64 years 65-72 years
Understanding disease networks 0-8 years 9-16 years 17-24 years 25-32 years 33-40 years 41-48 years 49-56 years 57-64 years 65-72 years
Disease risks are predictable! (85%-95% of incidences in population) Chronic diseases are in the center of the networks: risk factors for many other disorders Chronic diseases can not be “repaired”. But digital transformation allows to prevent and manage them Chmiel A, Klimek P, Thurner S, New J Phys 16, 115013 (2014)
Comorbidity networks and prevention Prevention of this Probability of causation: 0.73 disease hard €10 €1,000 Prevention easy Identify comorbidities Check causation Treat causing diseases
From Molecule to Man What is the optimal prevention strategy? Example HIV: In large cities social policies outweigh medical improvements
Beyond the pill Moment-by-moment quantification of the individual-level human phenotype in vivo using data from smartphones JP Onnela, SL Rauch, Neurpsychopharmacology 2016, in press
Data-driven prediction of drug effects and interactions • From adverse drug event reports to novel drug interactions • Automated pharmaco- epidemiological studies given ADE reports (adjusting for selection biases, sampling variances, confounders, ...) • First application to Stanford University Hospital: 47 new validated drug interactions NP Tatonetti, PP Ye, R Daneshhou, RB Altmann, Science Translational Medicine 2012, 4(125): 125ra31.
A network view on the healthcare system Nodes in the network = healthcare providers (HCP), for example doctors, pharmacies, hospitals, ... Links in the network = Connect HCPs that share patients Which flows of patients between HCPs are reasonable from a healthcare perspective? Healthcare mangement = shape this network such that it is efficient, sustainable, and resilient.
Patient flow networks Ideal example of a patient flow: Primary physician Specialist Pharmacy
Patient flow networks More realistic example... Primary physician Specialist Pharmacy Hospital
Austrian patient flow network Towards a data-driven era in health care management How robust is this system with respect to adverse events? How do changes to this system affect the efficiency of patient flows? Resilience of the health care system depends on its network structure.
Patient flow networks Geographically embedded simulation tool of the health care network What happens if you delete or add certain types of doctors? Resilience can then be quantified and assessed using network-based approaches
Economics and quality of health care • How do billing and prescription practices depend on such networks? • Results: Phyisicians adapt their behavior to match their colleagues identify inappropriate billings? • DA Kim, AJ O‘Malley, JP Onnela, submitted (2016)
Who should own your health data? • Currently it‘s not you. • With digital transformation the healthcare system becomes exposed to all kind of cyber vulnerabilities, including breaches of privacy. • Those we know how to deal with (though healthcare infrastructures might need to build up competencies in this area) • However, there is a different type of threat for which we currently have no response measures: • Using your health data to „game the system“
How to become a millionaire using health data (in 3 steps) Identify rare … treated Acquire diseases in with single rights for population… drug with drug and set Step 1 Step 2 Step 3 expired price as you patent but want no generic (insurance version. will pay anyhow)
Who should own your health data? How to become a millionaire using health data (in 3 steps) Identify rare … treated Acquire diseases in with single rights for population… drug with drug and set Step 1 Step 2 Step 3 expired price as you patent but want no generic (insurance version. will pay anyhow) Martin Shkreli and Turing Pharmaceuticals, February 2015
Conclusions Big Data allows for the first time to describe health care systems as „complex systems“ in a holistic way, paving the road towards a new type of medicine New era in health care management? Formulation of personalized prevention strategies and data-driven management of health care processes. But how should we decide who might use such data for which purpose?
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