BIG DATA: PHYSICIAN FRIEND OR FOE? Annual Health Law Conference - PowerPoint PPT Presentation

BIG DATA: PHYSICIAN FRIEND OR FOE? Annual Health Law Conference Northeastern University School of Law April 11, 2019 Barry Furrow Professor of Law and Director, the Health Law Program Kline School of Law @ Drexel University c. 2019

DATA ANALYTICS: PROGRESSIVE HEALTH CARE TRANSFORMATION A. DATA USE: A HISTORY OF GENIUS, DELAYS, AND PROGRESS B. DATA SOURCES: MORE DATA AND MORE COMPUTERING POWER C. DATA ANALYTICS: PANNING FOR GOLD D. BENEFITS OF BIG DATA I: PHYSICIANS E. BENEFITS OF BIG DATA II: HOSPITALS F. BIG DATA HURDLES: A LAWYER/ CURMUDGEON WORRIES G. SHOULD AI TAKE OVER? UNDER WHAT REGULATORY CONSTRAINTS?

This supercomputer will perform 1,000,000,000,000,000,000 operations per second.

A. DATA USE: A QUICK HISTORY OF GENIUS, DELAYS, AND PROGRESS

1. Florence Nightingale – Master of Big Data, Epidemiologist, Statistician, Graph Genius of Contagious Disease 1858 Nightingale was a talented and creative statistician. She returned from the Crimea with extensive data on soldier mortality rates. She completed her 850-page book Notes on Matters Affecting Health, Efficiency, and Hospital Administration of the British. Her statistical analyses reformed health and data collection in both military and civilian hospitals. Nightingale transformed data visualization. She developed the graphic method -- the polar area graph -- to convey information about causes of death during the Crimean War. Each of the twelve wedges was then divided into three colors: blue representing deaths from contagious diseases such as cholera and typhus, red representing deaths from wounds, and black representing deaths from all other causes. At a glance, the vast majority of deaths were from contagious diseases, which were largely preventable.

2. Ernest Codman, MD. Obsessive Collector of Patient Data 1917 “You hospital superintendents are too easy. You work hard and faithfully reducing your expenses here and there-a half—cent per pound on potatoes or floor polish. And you let the members of the [medical] staff throw away money by producing waste products in the form of unnecessary deaths, ill-judged operations and careless diagnoses, not to mention pseudo-scientific professional advertisements.“

• 1917. Dr. Ernest Codman - - ”End result survey”: every hospital should follow every patient that it treats, long enough to determine whether or not the treatment has been successful, and then to inquire 'if not, why not?' with a view to preventing similar failures in the future.” William Mallon, Ernest Amory Codman: The End Result of a Life in Medicine (Philadelphia: WB Saunders, 2000). • Goal: A complete patient record to evaluate, compare and establish benchmarks for the performance of physicians and hospitals .

CONCLUSION FROM THE SHORT HISTORY OF DATA IN HEALTH CARE: HOSPITALS AND OTHER HEALTH CARE INSTITUTIONS ARE S L O W LEARNERS

B. DATA SOURCES: MORE DATA AND MORE COMPUTING POWER IS NOW AVAILABLE

AHRQ (Agency for Healthcare Research and Quality: Bringing Predictive Analytics to Healthcare Challenge Learn about the $225,000 challenge to develop predictive analytics to estimate hospital inpatient utilization. Slow to the game and underfunded.

C. DATA ANALYTICS: PANNING FOR GOLD

KNOWLEDGE DISCOVERY PROCESS DATA MINING—CORE OF Pattern Evaluation KNOWLEDGE DISCOVERY PROCESS Data Mining Task-relevant Data Selection Data Warehouse Data Cleaning Data Integration Databases

DATA MINING LINKS THREE SCIENTIFIC DISCIPLINES (1) Statistics : the study of data relationships using numbers; Artificial intelligence : (2) the use of software and/or machines that display human-like traits; and (3) Machine learning : algorithms learning from data to make predictions.

D. BENEFITS OF BIG DATA I: PHYSICIANS

AI = PARTNER AND CONSULTANT .

AI HAS ALREADY OUTPERFORMED DOCTORS 1. Skin cancer diagnostics 58 dermatologists competed with a convolutional neural network. Doctors 86.6%; AI 95%. System developers used more than 100.000 pictures of this disease. Promise: early diagnosis of skin cancer. 2. Cancer and heart failure diagnostics Lab developed a machine vision system for diagnosing different types of cancer (breast, prostate, head and neck), epilepsy and heart failure. System correctly predicted heart failure 97%; doctors 74%. 3. Pneumonia diagnostics Stanford deep machine learning algorithm for chest X-ray images and detection of pneumonia. Can diagnose up to 14 types of medical conditions, and beat out 4 experienced radiologists. 4. Early diagnostics of cardiovascular diseases Oxford researchers system analyzes heart scans and predicts some diseases and possibility of heart attack more accurately than doctors.

1. Machine learning will dramatically improve the ability of health professionals to establish a prognosis 2. Machine learning will improve diagnostic accuracy

Stanford algorithm, CheXNeXt, is the first to simultaneously evaluate X-rays for a multitude of possible maladies and return results that are consistent with the readings of radiologists, the study says. Scientists at Stanford have trained the algorithm to detect 14 different pathologies: for 10 diseases, the algorithm performed just as well as radiologists; for three, it underperformed compared with radiologists; and for one, the algorithm outdid the experts.

AI = CARE EXTENDER. Telemedicine is obvious candidate. Here is a powerful and valuable use, coupling avatars for therapeutic and diagnostics, reading films, offering suggestions for patient self-care and directing patients toward live doctors for live care.

VIRTUAL HUMANS and AVATARS Virtual humans (VHs) improve clinical interviews, improving such screenings by increasing willingness to disclose information. Automated VHs can help overcome a significant barrier to obtaining truthful patient information. Research Report. Gale M. Lucas et al, It’s only a computer: Virtual humans increase willingness to disclose, Computers in Human Behavior 37 (2014) 94–100

E. BENEFITS OF BIG DATA II: HOSPITALS

AI = HUNTER FOR ERROR . Search for medical adverse events in hospital, variations in practice below the mean level of infections, mortality, patient satisfaction. Report cards are then step 1; staff privilege actions are step 2. Must such data profiling be send to the National Practitioner Data Bank? If not, what should be sent as data mining reveals the poor doctors, the incompetents, the substandard?

DATA ANALYTICS WILL DISCOVER PHYSICAN- CAUSED ADVERSE EVENTS. WILL HOSPITALS STEP UP THE HUNT FOR BAD “DOCS”? SHOULDN’T THEY?

AI Reduces Adverse Events Adverse events among inpatients at three leading hospitals are 33.2 percent of hospital admissions for adults, up to ten times previous studies. David C. Classen et al, ‘Global Trigger Tool’ Shows That Adverse Events In Hospitals May Be Ten Times Greater Than Previously Measured, 30 Health Affairs 581 (2011) (uses global trigger tool, a form of chart review that searches for triggers that mark adverse events.) “Operation of a common automated ADE surveillance system across hospitals permits meaningful comparison of ADE rates in different inpatient settings. Automated surveillance detects ADEs at rates far higher than voluntary reporting…” Peter M. Kilbridge, Udobi C. Campbell, Heidi B. Cozart, Maryam G. Mojarrad, Automated Surveillance for Adverse Drug Events at a Community Hospital and an Academic Medical Center

AI Identifies Hospital Acquired Conditions (HAC) “Leveraging machine-learning, analytics can identify emerging complications and alert clinicians to prevent serious harm to patients, excessive costs and long-term healthcare needs.” The focus on data has made a dramatic impact in contributing to a reduction in hospital acquired infections, which not only results in better delivery of care but also improved financial performance. Major causes for HACs have steadily declined since 2008.” Health Care Analytics: How Data is Changing Everything, Conduent Business Services 2017, https://downloads.conduent.com/content/usa/en/ebook/healthcare- analytics.pdf

WILL DOCTORS AS EMPLOYEES BECOME MORE VULNERABLE? AS INDEPENDENT MEDICAL STAFF? WILL DATA ANALYTICS FINDINGS CIRCUMVENT PEER IMMUNITY STATUTES? See Barry R. Furrow, Searching for Adverse Events: Big Data and Beyond, 27 Annals of Health Law 149 (2018)

AI = GATEKEEPER OF FUTILITY JUDGMENTS . First comes determination of threshold for palliative care, reducing otherwise standard treatments. Sounds good. Then comes insurance incentives to find this out, to use it to demand less care, or even drop patients. Not likely?