Public Health and Regional Informatics Mark Frisse November 18, 2008 see: lecture

what we will cover What is public health? What is regional informatics? What are the common themes? What are the challenges? What are the research and service opportunities?

differences and similarities populations, not individuals prevention more than diagnosis and treatment government more than providers same! my claim is that the informatics issues required to address public health are the same as those for many other pressing clinical problems

the textbook answers three functions Assessment involves monitoring and tracking the health status of populations. Policy development utilizes the results of assessment activities in concert with local values and culture to recommend interventions and policies that improve health status. Assurance refers to the mission public health agencies have to assure constituents that services necessary to achieve agree-upon goals are provided.

big deals unsuccessful siege of the Assyrians against Jerusalem (701 BCE) guns, germs, and steel the great influenza HIV drug-resistant TB, Staph, other stuff immunizations

more big deals lack lung in miners asbestos workers back injury and other occupational-related disorders - $2.3 billion dollars fluoridated water changed dentistry seat belts high fat foods tobacco

my view relationships people have with one another coordinated systems of prevention, detection and care analysis and presentation of signals interventions

reporting federal & state issues completeness accuracy efficiency latency privacy and confidentiality

federal (examples) AIDS Amebiasis Anthrax Aseptic meningitis Asthma, work-related Botulism Chancroid Chlamydia trachomatis genital infection Cholera Coccidioidomycosis Cryptosporidiosis Cyclosporiasis Denguer Diphtheria HIV Influenza Kawasaki Disease Legionellosis Leptospirosis Listeriosis Lyme disease Lymphogranuloma venereum (LGV) Malaria Measles, total Meningococcal disease Mumps Neurosyphilis Non-gonococcal urethritis (NGU) PID Pertussis Plague Poliomyelitis Rabies Salmonellosis Shigellosis Silicosis Smallpox Spinal cord injury Strep. pneumoniae, invasive disease < 5 years Streptococcal disease, invasive, group A Streptococcal toxic- shock syndrome Streptococcus pneumoniae, drug- resistant Syphilis TetanuToxic-shock syndrome (other than streptococcal) Toxoplasmosis Trichinellosis Tuberculosis Tularemia Typhoid fever Typhus fever Vancomycin-resistant Staphylococcus aureus (VRSA) Varicella

not all are infections Head injury Lead poisoning Motor vehicle injury Sudden infant death syndrome Suicide

tennessee

ph-1600

completeness automatic reporting of health conditions may lead to 4x the number of incidents this means 4x as much work for public health professionals… unless…you can give them access to a community-based record

cool people and projects McMurray, Kohane, Mandl Grannis and Overhage Mostashari Wagner

SPIN (McMurray et. al.)

SPIN features is self-scaling, voluntary and hence may be applicable to a national network employs a distributed approach to data storage that they argue minimizes breach and hence protects privacy. maintains institutional participation because of the autonomy relegated by a distributed approach. provides oversight and transparency

cdc public health informatics grid a need for wide distribution of public health data. rapid growth of public health data. cultural, social and political impediments to data sharing. significant and chronic financial constraints. a dynamic and complex environment - global in scale. an environment containing many redundant systems, as well as application and data silos. an environment with a wide variety of complex requirements (disease surveillance, alerting, event detection, etc).

surveillance: retail-style

new ways

analysis: signal vs. noise Analysis Case detection algorithms Time-series methods Combining multiple signals Spatial and spatial-temporal clustering Modeling

markle principles openness and transparency. accountability and oversight individual participation and control purpose specification and minimization collection limitation use limitation data integrity and quality security safeguards and controls legal and financial remedies for violations

cholera

whosissick.org

memphis

Baptist Memorial Health Care Corp. (4 facilities) Christ Community Health (4 primary care clinics) Methodist Healthcare (7 facilities including Le Bonheur Children’s Medical Center) The Regional Medical Center (The MED) Saint Francis Hospital & St. Francis Bartlett (Tenet Healthcare) St. Jude Children’s Research Hospital Shelby County/Health Loop Clinics (11 primary care clinics) UT Medical Group (300+ clinicians) Memphis Managed Care/TLC (MCO) The MidSouth eHealth Alliance

After 18 months of operation Total # of encounter records: 3.9 million Total # of patients: 1,050,000 Total # of patients with clinical data: 930,000 Monthly Encounter Data: 140,000 Monthly ICD-9 admission codes (Chief complaints): 34,000 Monthly labs: 2,400,000 Monthly microbiology reports: 26,000 Monthly chest x-ray reports: 35,000 Comprehensive privacy agreements Costs to participants less that $50,000 per hospital Overall annual operating cost – under $3 million

Architecture Exchange receives data & manages data transformation Mapping of Data Parsing of Data Standardization of Data Queue Management Data is published from data source to the exchange Participation Agreement Patient Data Secure Connection Batch / Real-Time Organizations will have a level of responsibility for management of data Issue Resolution Data Integrity Entities are responsible for managing their Data Data bank compiles and aggregates the patient Data at the regional level Compilation Algorithm Authentication Security User Access

Use > 400 users Low in ED (< 5%) Growing use in safety net clinics hospitalists usage low Increasing connectivity to ambulatory sites Reduces redundant tests; impacts care

visualization?

public health / HIE issues completeness accuracy efficiency latency privacy and confidentiality

completeness more data but more ways of managing information at the point of decision-making

accuracy clear data integrity checks because the data are the same used for clinical care

efficiency data are collected “on the margin.” you no longer have separate systems, you have one, single, amorphous system whose use is dictated by need and authorization everything becomes a marginal cost

latency detection? none…nada…zip only the time it takes the brain to process and the system to intervene

privacy - agreements openness and transparency. accountability and oversight individual participation and control purpose specification and minimization collection limitation use limitation data integrity and quality security safeguards and controls legal and financial remedies for violations

the real lesson our health care system is broken our health care system is fragmented wherever you go - be it personal health, pay- for-performance, public health, information exchange, or public policy - you face the same issues a unified approach based on a very simple, extensible technical and policy framework seems, in my mind, to be the only way informatics can help enable the health care system we all want and need.