Week 03- Merged Slides.pdf

Provider Analytics Providers Primary Data Types PATIENT DATA (PATIENT MEDICAL RECORDS, PII) CLINICAL RESEARCH DATA FINANCIAL AND OPERATIONAL DATA 2 Motivators for Information Security and Privacy Protections Financial liability from exposure of PII Regulatory compliance Providers have the most diffi...

Provider Analytics Providers Primary Data Types PATIENT DATA (PATIENT MEDICAL RECORDS, PII) CLINICAL RESEARCH DATA FINANCIAL AND OPERATIONAL DATA 2 Motivators for Information Security and Privacy Protections Financial liability from exposure of PII Regulatory compliance Providers have the most difficult time with information security due to various devices and personnel involved, as well as the high number of outsourced service partners they share data with 3 Data Leaks Inadvertent data leaks have become common due to permissioned access to data sets Cleanup costs of data leaks have grown considerably, including lost business, reputational damage, identity monitoring services, and forensic investigation costs Malicious data breaches are of more concern to most providers and can result in considerably higher costs 4 Patient Privacy Patient privacy will be an area of focus to ensure actionable insights can be used without objection from patients IT has traditionally been responsible for information security, but as new analytical techniques use more PII, legal departments will need to be involved in deciding what levels of permission must be secured to use insights freely Providers who intend to become analytical competitors will need to shift their focus from securing data to seeking permission to use insights 5 Guidance If analytical results are shared beyond normal boundaries, and identity can be detected, the use of those results needs to be reviewed before acting Providers need to initiate a dialogue with IT to ensure they don't get blindsided by a privacy suit It is important to seek proper permission from patients to use medical records for specific purposes Providers who want to become analytical competitors need to shift their focus to seeking permission to use insights generated from their data 6 Payer Primary Data Types Patient data Actuarial and rate information (patient medical records, PII, patient financial records) 7 Payer Organizations Collect and make rate and coverage decisions using PII to predict insurance risks and set premiums accordingly Analytics mostly applied at an aggregated data level, but atomized data will become more accessible with analytics, making information security and privacy critical 8 Legal Teams vs IT Teams Leading payer organizations involving legal teams more than IT teams as their ability to take action hinges on satisfying privacy rights among the insured community and regulators HIPAA compliance will go up if consumers complain that new decisioning techniques can better assess an individual's risk, producing higher premiums for example: Claims of privacy violations could slow more efficient decisioning and classification 9 Guidance Negative reactions by consumers to rate increases and uncertainty around the ultimate outcome of the federal healthcare legislation are putting pressure on providers to price both existing and new policies correctly Scenarios could be imagined where consumer health advocates could claim privacy over patient data used to drive toward rate increases or policy changes 10 Data Protection in Life Sciences Organizations Pharmaceutical and biotech organizations protect highly proprietary research and development information, including clinical trial results and drug formation results. Focus on data leakage (securing the perimeter) and database security (access, encryption). 11 Analytics and Security Risks Analytics increase calls on large databases by numerous internal and external parties, somewhat increasing security risks. Security infrastructure in most life science companies was built with scale as the number one priority. Privacy is less of an issue since clinical trial data are largely aggregated and anonymized from the beginning. 12 Guidance Information security infrastructure should grow to match the pace of proprietary information creation with analytics Success in life sciences organizations is having an adequate data inventory and knowing where new proprietary information is being stored 13 Introduction to Patient Analytics Patient analytics involves use of data analytics and business intelligence tools to gain insights and improve healthcare outcomes for patients Enables personalized, data-driven care that is tailored to individual patient needs Patient data can come from a variety of sources, including electronic health records, medical devices, wearables, social media, and other digital sources. 14 Applications of Patient Analytics Monitor Monitor patient health Identify Identify high-risk patients Track Track patient progress Measure Measure effectiveness of treatments Optimize Optimize clinical workflows Improve Improve patient outcomes Reduce Reduce healthcare costs 15 Benefits of Patient Analytics Enables personalized, data-driven care Identifies patterns, trends, and anomalies to inform clinical decisionmaking Improves patient outcomes Reduces healthcare costs 16 Challenges of Patient Analytics PRIVACY AND SECURITY CONCERNS DATA QUALITY AND COMPLETENESS INTEGRATION WITH EXISTING CLINICAL WORKFLOWS TECHNICAL EXPERTISE AND RESOURCES 17 Future of Patient Analytics Continued growth and adoption Integration with other emerging technologies (e. g. telemedicine, artificial intelligence) Increased focus on patient-centered care and outcomes 18