AAB-HC-S1 21 March KOL Class.pdf

Applied Analytics for Business – Healthcare Dr. Ravi Shankar Prof – Data Science (AI -ML) Dean – Enterprise Solutions & Academic Collaborations https://www.linkedin.com/in/ravi-shankar-70584b1/ [email protected] First Session , 21st March, 2023 Introduction to HC • Problems & Challenges • Stakeh...

Applied Analytics for Business – Healthcare Dr. Ravi Shankar Prof – Data Science (AI -ML) Dean – Enterprise Solutions & Academic Collaborations https://www.linkedin.com/in/ravi-shankar-70584b1/ [email protected] First Session , 21st March, 2023 Introduction to HC • Problems & Challenges • Stakeholders & their interactions • AI-ML in HC Faculty Profile : Dr. Ravi Shankar INTRODUCTION • Background: PhD - Econometrics, Entrepreneur, Start-up Mentor, Social Impact Investor, Data Science Thought Leader, Senior Venture Partner • Current Focus: AI adoption in Business, Deep Learning, XAI, ML Operations, Design Thinking, OB & Strategy • Sectoral Exposure: multiple sectors straddling BFSI / Pharma / Manufacturing / Retail / Tech • Overall Experience : 30 Yrs. Applied Analytics for Business - Healthcare: Learning Journey Five sessions of 1.5 hours each Fourth session – First session Second session – – Introduction Healthcare 4.0 Third session – Data Governance Fifth session – AI- ML Real World AI- ML Real World Applications – I Applications – II Mortality Prediction Suicide Rate Trend analysis STRUCTURE MOTIVATION TRENDS ECOSYSTEM STAKEHOLDERS HC ANALYTICS APPLICATIONS MOTIVATION 1. The daunting challenges facing the healthcare industry today make for compelling arguments to expand the role of analytics. 2. Evidence continues to mount that healthcare is increasingly challenged by entrenched inefficiencies, including wasting more than US$2 trillion annually. These inefficiencies can be attributed to several factors, including the ineffective gathering, sharing and use of information. 3. Clinical outcomes remain suboptimal, particularly in the United States, where 96 people per 100,000 die annually from conditions considered amenable to healthcare. 4. Hospitals in Australia, Canada, Denmark, France, New Zealand, Spain, the United Kingdom and the United States have reported high levels of preventable errors. Error rates ranged from 2.9 to 45.8 percent for hospitalized patients, of which 7.6 to 51.2 percent were preventable. 5. In addition to systemic issues, other factors add to the immense complexity the healthcare industry is facing. Citizens have higher expectations of their healthcare providers, have more access to information than ever before, and are demanding increasing accountability from their doctors, nurses and health plans. In fact, from the consumer point of view, health plans ranked last among 14 industries in a customer experience survey, trailing even television and Internet service providers, and well behind other insurance providers. 6. The increasing regulatory presence of government places additional focus on accountability, governance and oversight on the industry. Market dynamics and competitive pressures require enhanced understanding of underlying trends and a path to differentiation. Top performers: “Pros” use analytics to differentiate, see the future and drive revenue growth The path to value operationalizes analytics. What the future demands: Best practices in getting started or accelerating your journey along the path to analytics competency Motivation : Q’s How are healthcare provider and payor organizations applying analytics today, and how might they need to think about its future use? How do high performing organizations use it differently than their peers? What are the barriers to adoption? What forward-looking analytics innovations can healthcare organizations apply to meet their mounting challenges? Why analytics competency is more important than ever Benefits Predictive Analytics In Healthcare Care for High-Risk Patients Satisfaction of patients Deal with Human Error Industry Advancement Cost Reduction Analytics Analytics is the systematic use of data and related business insights developed through applied analytical disciplines (e.g. statistical, contextual, quantitative, predictive, cognitive, other [including emerging] models) to drive fact-based decision making for planning, management, measurement and learning. Analytics may be descriptive, predictive or prescriptive. What is HC Analytics? “Healthcare analytics is the process of analyzing current and historical industry data to predict trends, improve outreach, and even better manage the spread of diseases. The field covers a broad range of businesses and offers insights on both the macro and micro level. It can reveal paths to improvement in patient care quality, clinical data, diagnosis, and business management”. HC Analytics Reduce costs Improve coordination with care teams Provide more with less Focus on improving patient care HEALTHCARE ANALYTICS THEN & NOW 1854 CHOLERA ENDEMIC, LONDON - Rudimentary cluster mapping - Manual and inaccurate analysis 2014 EBOLA EPIDEMIC - (Biomosaic tool), CDC Emergency Response Center - Sophisticated predictive modeling - data from mobile phones, historical epidemiological data - Multiple data sources Florence Nightingale - Wikipedia - High computing power • Analytics can help to explore issues and determine the best methods to make effective progress. Mitigate risk • Analytics can help cut through complex datasets, providing key information to deliver better results in less time. Enact sustainable change Deliver quicker outcomes Benefits of healthcare analytics Assignment : Undertake secondary online research to identify three real world examples / use cases • Analytics can carefully measure and evaluate data to drive clinical and operational improvements. Must-Know Healthcare Statistics in 2022 • Have you ever wondered how big the healthcare industry is? • Healthcare takes more than 10% of the GDP of most developed countries. • In fact, for the US this figure will be close to 18% by the end of 2022. This isn’t surprising—the healthcare sector is the US’s largest employer. Incidentally, the US spends considerably more than the world’s average on healthcare. • As we can see, recent healthcare statistics show that it’s one of the largest and fastestgrowing industries in the world. • • • • • • • • • The global health industry was worth $8.45 trillion in 2018. Global healthcare spending could reach over $10 trillion by 2022. The US has the greatest healthcare spending, sitting at $11,224 per capita. The US spends twice what other countries do on healthcare. There are 784,626 companies in the US healthcare sector. McKesson is the biggest US healthcare company with an annual revenue of $208.3 billion. The internet of things (IoT) can lower the costs of operational and clinical inefficiencies by $100 billion per year. 64% of physicians believe the IoT can help reduce the burden on nurses and doctors. 28% of China’s population uses connected health devices, the highest in the world. Problems in healthcare – High Cost > Assignment: write a three page note on why Google exited the HC business. Problems in healthcare – High Waste = $765 Billion dollars Total budget for 50 years Problems in healthcare – Low Quality • 200,000 - 400,000 preventable deaths per years in USA • 3rd Largest risk factor Big data Analytics in Healthcare According to International Data Corporation (IDC) ▪ Big data analytics is projected to grow faster in healthcare than in sectors like manufacturing, financial services or media. ▪ Estimated Compound annual growth rate (CAGR) of 36 percent through 2025. ▪ Global big data in the healthcare market is potential $34.27 billion by 2022 at a CAGR of 22.07%. Data sources Accelerating industry change The COVID-19 pandemic is placing enormous strain on the global health care sector’s workforce, infrastructure, and supply chain, and exposing social inequities in health and care. COVID-19 is also accelerating change across the ecosystem and forcing public and private health systems to adapt and innovate in a short period. Healthcare analytics trends that are shaping the industry in 2021 Financial analysis and revenue cycle management Population health and care management Clinical operations Provider performance Top 5 Healthcare Analytics companies 2021 | Report Analysis 2021-2027 (brandessenceresearch.com) The next wave of healthcare innovation: The evolution of ecosystems What could the healthcare ecosystems of the future look like? What are the component layers that will form future healthcare ecosystems? How can healthcare stakeholders prepare for and act within healthcare ecosystems? How can healthcare stakeholders prepare for and act within healthcare ecosystems? Technology upgrades to leverage increasing data liquidity. Operating model upgrades to drive insights through data and analytics Data-first talent model upgrades to capture value. External and partner services upgrades to expand engagement. Payers Determine which ecosystem or subecosystem to curate Build partnerships that will allow stakeholders to create a seamless experience for patients Integrate patient and provider services into the ecosystem through contract, partnership, or acquisition on a usecase basis and with incentives in mind Providers Develop a strategy for bringing together care experiences across the entire continuum Re-work the traditional concept of organization via “service lines.” Integrate tools and care approaches that address non-clinical behaviors that influence health status, potentially through partnerships with other organizations Deploy tools that help personalize the ecosystem experience for each individual patient. Healthcare services and technology players At the intelligence layer: At the infrastructure layer: healthcare services and technology players (for example, health information exchanges and clinical information systems) currently provide data collection, transfer, and management capabilities. As the layer of infrastructure underpinning healthcare ecosystems matures—including through the entry of large technology giants— these healthcare services and technology players can realize value by building capabilities that require healthcare-specific expertise and domain knowledge to serve critical functions in enabling data sets. healthcare services and technology players (payment integrity, revenue cycle management, population health, clinical decision support) currently play a critical role in converting underlying data to actionable insights for a variety of customers. With the evolution of healthcare ecosystems, the opportunities for intelligence functions will likely expand materially. As advanced analytics capabilities mature—including through healthcare agnostic technologies—healthcare services and technology players can build off these capabilities and data to develop healthcare-specific insights. These insights can be provided to the patient in an efficient and actionable way, while also improving the quality of care. At the engagement layer: healthcare services and technology players, including patient engagement, care and disease management, utilization management, and provider enablement, currently play a critical role in providing information to and changing behavior of healthcare stakeholders. As healthcare ecosystems evolve, the number and complexity of points of engagement will continue to expand. This expansion presents an opportunity for engagement organizations to leverage an increasing amount of data and actionable insights and create value through patient behavior and payer/provider behavior. These players will need to learn how they can best plug into broader healthcare ecosystems to drive adoption and engagement. https://vimeo.com/108627029 https://mm.tt/1995409885?t=DuPQSj20hm Healthcare Analytics Applications . Healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve the quality of life in general. . Pharmaceuticals 1) Patients Predictions For Improved Staffing If you put on too many workers, you run the risk of having unnecessary labor costs add up. Too few workers, you can have poor customer service outcomes – which can be fatal for patients in that industry. Applications that demonstrate how an analytical approach can improve processes, enhance patient care, and, ultimately, save lives: 2) Electronic health records (EHR) • Every patient has his own digital record which includes demographics, medical history, allergies, laboratory test results, etc. • Every record is comprised of one modifiable file, which means that doctors can implement changes over time with no paperwork and no danger of data replication. • EHRs can also trigger warnings and reminders when a patient should get a new lab test or track prescriptions to see if a patient has been following doctors’ orders. 3) Using Health Data For Informed Strategic Planning • The use of big data in healthcare allows for strategic planning thanks to better insights into people’s motivations. • Care managers can analyze check-up results among people in different demographic groups and identify what factors discourage people from taking up treatment. 4) Reduce Fraud And Enhance Security • Analytics helps to prevent security threats by identifying changes in network traffic, or any other behavior that reflects a cyberattack. • It can help prevent fraud and inaccurate claims in a systemic, repeatable way. • Analytics helps to streamline the processing of insurance claims, enabling patients to get better returns on their claims and caregivers are paid faster Healthcare Applications Applications – Computational Phenotyping Applications – Predictive Modelling Patient similarity Patient similarity Patient similarity Patient similarity Advantages of Predictive Analytics include: A. Increases Diagnostics Accuracy B. Focus on Preventive Medicine C. Customisation for Individual Patients D. Beneficial for Pharma Companies Advances of ML & AI in Healthcare – Image Analytics • Goal : Bring expert diagnosis to places where trained doctors are in scarce • Aim : Diagnosing diabetic retinopathy • Google working on developing the deep learning models Helping doctors predict medical events • Is my patient likely to get better • Is my patient likely to go home soon • Is my patient likely to get sick again Big Data Analytics Predictive Analytics promises to improve healthcare by forecasting the likelihood of an event, healthcare providers to take pre-emptive action where possible. Predictive Analytics uses statistical analysis and other techniques to search through reams of patient data.. and analyses it to predict outcomes for individual patients.. Data from past treatment outcomes as well as recent medical research data aid predictive analytics tools in arriving at the inferences.. There are two major differentiators between Predictive Analytics and traditional statistics: A. Predictions are made at an individual level and not at a group level B. Statistical tools rely on “bell curves” and their recommendations go by where the largest sample lies Key Data Drivers for Predictive Analytics: A. Medical and Patient Data B. Big Data Analytics C. Mobile/m-Health D. Healthcare Professional Digital Workflow Thank you…………