Design solutions for building a rapid population health

transcript

Design solutions for building a rapid population health analytic platform using PostgreSQL and

PostGISJanos G. Hajagos

Department of Biomedical InformaticsStony Brook University

PGCONF 2015March 27, 2015

DSRIP (Delivery System Reform Incentive Payment )

• New York’s CMS Medicaid 1115 Waiver (April 2014)• Based on performance metrics New York State and health care providers could receive up to $8 Billion

• Move health care from fee‐for‐service to value‐based payment mode• Performing Provider System (PPS) most take a data driven to meeting metrics

• Integrate care across multiple setting from the inpatient to the outpatient

• Application from each PPS is graded and grades will determine potential payout

Suffolk Care Collaborative

Time, Space, Licensing the Final Frontiers

• NY State and SUNY have licenses for most commercial software at little or minimal cost (Oracle, MSSQL, ArcGIS, SAS)

• Getting the details on the licenses and restrictions (classroom versus production use) is a challenge

• Ordering licenses and getting purchase orders takes time• Installing commercial software is more complicated (configuration, license files, license activation, license servers)

• Keeping track of license can be a real pain• My analytical group had a very tight deadline to meet deliverables!

Can Open Source Software Solutions Compete?

• Health care IT and data analytics solutions traditionally have been propriety and expensive

• We still run mainframes!• SAS ever looked at the price

• Health care IT is driven by fear: • Legal liability• Privacy data breaches (HIPAA)

• Population health outside of direct patient care context can be a proving ground for Open Source solutions

• PostgreSQL is a mature database platform• It is actively developed and has momentum• Easy to install on common Linux distributions and even Windows Server

• PostGIS spatial extension is robust and mature• ANSI Standard SQL Support• I will show how it can be used to build a rapid population health analytic platform

Image source: https://www.flickr.com/photos/pelegrino/2450972179/

PostgreSQL

Deployment Details

• System set up in May of 2014• PostGreSQL 9.3 – Ubuntu 12.04• Deployed within the existing Hospital IT infrastructure• VMWare virtualized environment• 8 Cores• 20 Gigabytes of RAM• 4 terabytes of data storage on a SAN

Outline of Talk

1) The use of schemas for the management and loading of multiple data sources.2) How to process and analyze hospital discharges using the range data functions and operators.3) The integration of the American Community Survey (ACS) data and the geocoded address with PostGIS so as to understand regional differences in health care delivery in Suffolk County, NY.4) Computing behavioral health comorbidities for hospital inpatients using SQLAlchemy, CCS codes and Tableau

Schemas for Fine Grained Access Control

Temporal data – transitions of care

Health Care Data is Often Messy

The assumption is that inpatient stays do not overlap:

In reality data from an Electronic Health Record will look like this:

Deep Breath – PostgreSQL has you covered

Image source: https://www.flickr.com/photos/asifhaque/3078893001/

Representing an Inpatient Stay as a Range

[14, 18][14, 19) *

A patient can be discharged at 11:59 pm

Dealing with Date Ranges for Inpatient Stays

• Convert to Julian Day• cast(to_char(cast("Admission Date" as date), 'J') as int) as start_julian_daycast(to_char(cast("Discharge Date" as date), 'J') as int) as end_julian_day

• Constructor ‐ int4rangeint4range(start_julian_day, end_julian_day + 1, '[)')

• Query uses the following operators:• && Overlaps• <> Not equals

A Rich Set of Range Types Operators

http://www.postgresql.org/docs/9.3/static/functions‐range.html

Computing a Normalized Inpatient StayUPDATE inpatient_admission_test iat0 SET

union_day_range = t.union_day_range

FROM (

SELECT iat1.transaction_id, iat1.union_day_range + iat2.union_day_range AS union_day_range

FROM inpatient_admission_test iat1

JOIN inpatient_admission_test iat2 ON

iat1.patient_id = iat2.patient_id AND iat1.transaction_id !=iat2.transaction_id AND

iat1.union_day_range && iat2.union_day_range and iat1.union_day_range <> iat2.union_day_range) t

WHERE iat0.transaction_id = t.transaction_id;

Iteratively Updating union_day_range

Pair a Patient’s Inpatient Stay

SELECT lower(ier1.union_day_range) ‐upper(ier2.union_day_range) as days_since_paired_discharge,

ier1.patient_id, ier1.union_day_range as target_union_date, ier2.union_day_range as previous_target_union_date,

ier1.id as target_id, ier2.id as previous_idFROM inpatient_event_ranges ier1 JOIN inpatient_event_ranges ier2 ON ier1.patient_id

= ier2.patient_id ANDier2.union_day_range << ier1.union_day_range;

Chained Inpatient Stays

In Conclusion

• In less than 70 lines of codes we have normalized and linked a patient’s inpatient stays

• Code has been applied to 100,000 discharge data set• We are currently using this data set for building predictive analytic models for 30‐day same hospital readmissions

• Synthetic inpatient data and SQL code developed at• https://github.com/jhajagos/SynthMedTopia/

United States of America

NAD83(NSRS2007) / New York Long Island ‐ Projection

Adding Layers from PostGIS in QGIS

Suffolk County, New York

Suffolk County with Postal Code Regions

Western and Central Suffolk County

11746 – Huntington Station and Dix Hills

The Tale of Two Census‐Designated Places

Geocoding• Tiger based geocoder can easily be installed• Requires PostGIS extension to be installed first• Script downloads street address data from the Census Bureau’s website• A good start is at: http://gis.stackexchange.com/questions/81907/install‐postgis‐and‐tiger‐data‐in‐ubuntu‐12‐04

• In my experience it works better with residential addresses than business addresses

• When it fails it fails badly• Needs a second level check on the quality of match

• No limits and no privacy issues

Using the Tiger Geocoder

SELECT (tt.geo).geomout, (tt.geo).rating, ST_Y((tt.geo).geomout) as latitude, ST_X((tt.geo).geomout) as longitude, tiger.pprint_addy((tt.geo).addy) as

matched_address, (tt.geo).addy.zip as matched_zip5FROM (select

tiger.geocode(‘?? Suncrest Dr., Dix Hills, NY 11746', 1) as geo) tt;

Geocoding Results

Median Household Income – Census Tracts

33ACS Variable: B19001

Percent of Households where Spanish is the Primary Language

34ACS Variable: B16002

New York City

Loading Shape Files

• Shapefiles (shp) and dBase dbf files • Download the appropriate shapefiles

• TIGER/Line Shapefiles FTP site

• GUI / CMD line tools > shp2pgsql ‐s 4269:4269 ‐g geom ‐I ‐W LATIN1 tl_2013_us_county.shp spatial.us_counties > ~/us_counties.sql> psql dsrip < us_counties.sql

Loading American Community Survey data

• Start with American Fact Finder:• http://factfinder.census.gov/

• I created a tool in Python for preprocessing and bulk loading ACS data into PostGreSQL

• https://github.com/jhajagos/CensusGeographyTools

• The geoid allow joining of ACS variables to shapefiles

Spatial Joins ‐ Freedom from Postal CodesSELECT latitude, longitude, ge.matched_address, statefp, geoid, stl.namelsad

FROM public.geocoding_example ge JOIN spatial.tl_2013_36_tract stl ON ST_intersects(ge.geomout, stl.geom);

SELECT latitude, longitude, ge.matched_address, statefp, geoid, stl.namelsad

FROM public.geocoding_example ge JOIN spatial.tl_2013_36_bg stlON ST_intersects(ge.geomout, stl.geom);

ST_intersection

CREATE table ny_county_trimmed_to_land asSELECT tb.gid, tb.statefp, tb.countyfp, tb.geoid,

tb.namelsad as name,ST_intersection(lp.new_york_state_land_area_geom,

tb.geom) as geomFROM spatial.us_counties tb,

new_york_state_land_area lpWHERE tb.statefp = '36';

Spatial Shape Processing

• PostGIS supports Open Geospatial Consortium (OGC) standard• Allows more sophisticated processing of spatial shape data

• Bounding boxes• Intersections• Unions• Finding midpoints

43https://www.flickr.com/photos/kulakovich/2152075315

SPARCS ‐ Statewide Planning and Research Cooperative System• Inpatient discharges are uploaded from every acute care hospital in New York State

• Data is processed and stored by the New York State Department of Health

• Data is made available in several forms:• Fully identified• Obscured dates (month only) and hashed personal identifiers• Personal identifiers and month and day removed

• Used throughout the state for research and health care system planning

Health care Data Files are Delivered Flat

SPARCS Table Normalization using SQLAlchemy• Load SPARCS data into PostgreSQL:

• https://github.com/jhajagos/ny_sparcs_import

• Generates SQL on the fly to normalize repeated columns• https://github.com/jhajagos/agile_data_tools/blob/master/normalize_table_from_columns_that_repeats.py

• SQLAlchemy, a Python library, allow introspection of table structure and data types

• Python has several mature PostgreSQL drivers• Pg8000 – pure Python based library• Psycopg2

Connecting to traditional BI tools

• Tableau

Final Goal is Insight into the Population

Parting Thoughts

• Health care data for population health is not that big• Big data platforms do not currently offer fine grained spatial, temporal, and data handling

• SQL still rules / OGC spatial extensions

• Health care data analyst need to develop a community of sharing• Generation of synthetic data

• Keep improving PostgreSQL!

Acknowledgements• Stony Brook University Department of Biomedical Informatics• Stony Brook Medicine Information Technology

Design solutions for building a rapid population health

Healthcare