clinical informatics john welton, phd, rn, faan cu college of nursing bios 6660 university of...
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Clinical InformaticsJohn Welton, PhD, RN, FAANCU College of Nursing
BIOS 6660
University of Colorado College of Nursing
November 3, 2015
Big Data Concepts
Data Explosion
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Challenges
▪ Storage, processing, computational limitations
▪ Security, confidentiality, privacy
▪ Obsolescence of current technology
▪ Accessing data across multiple settings
http://blog.codinghorror.com/content/images/uploads/2006/01/6a0120a85dcdae970b0128776fd5cc970c-pi.png
http://oldcomputers.net/pics/osborne1.jpg
Big Data Concepts
▪ Volume
▪ Velocity
▪ Variety▪ Diverse representations of data▪ Complexity and multiple/mixed
media, e.g. video, sound, pictures, texting, Twitter, Facebook, etc.
▪ Autonomous data sources with distributed and decentralized controls
Wu, X., et al. (2014) Data mining with big data. Knowledge and Data Engineering, IEEE Transactions on 26, 97-107
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Items and Issues
▪ Data accuracy and missing data
▪ Extraction and common data models
▪ Archiving and persistence of data
▪ Data consistency across time and settings
▪ Version control, obsolescence
▪ Structured vs. unstructured data
▪ Lack of common data model
▪ Lack of IT support (resources)
▪ Lack of expertise in working with large data
▪ Resources needed to manage “the machine”
Healthcare Data?
▪ Assessments, Physical Exam
▪ Order entry
▪ Results reporting: Labs, xrays, pharmacy (prescription)
▪ Flow sheet data, vital signs, point of care testing
▪ Problem list, treatment plan
▪ Diagnosis, billing, reimbursement
▪ Staffing/assignment (workforce)
▪ Medication administration (bar code)
▪ RFID
Some Interesting Data
▪ RFID (time and position)▪ Tracking patients and
nurses/personnel▪ Finding resources
▪ Call light and response
▪ Continuous data streams from devices, e.g. monitors, beds, etc.
▪ Medication administration (BCMA/eMAR)
http://www.rfidc.com/docs/indoor_rfid_tracking.htm
What are the “Big” Healthcare Questions
Clinical/Patient Focus
▪ Improve health/nursing care
▪ Optimize outcomes
▪ Population management
▪ Better patient experience
Operational/Organizational Focus▪ Healthcare workforce
▪ Resource utilization
▪ Costs, quality, value
▪ Performance, efficiency and effectiveness
Other/Healthcare System Focus
▪ Payment
▪ Policy, etc
Research Perspectives
▪ Continuous data streams
▪ Large volumes of clinical / operational data
▪ Complete data on entire population
▪ Span multiple clinical settings
▪ Examine all provider “touch points”
▪ Multiple/simultaneous natural experiments
Rethinking Healthcare Research
▪ Very large and complex data systems (volume)▪ Statistical significance of large data▪ Time referenced data (e.g. stock
market)
▪ Sipping from a fire hose (velocity)▪ Continuous data streams▪ Natural experiments
▪ Large data sets Complex data sets (variety)▪ Span multiple settings▪ Complex questions and answers
Rethinking Healthcare Systems
Clinical
▪ Real-time clinical decision making
▪ AI potential for pattern recognition
▪ Mapping trajectories of care
▪ Acuity trending (patient, unit, hospital/agency)
Operational
▪ Real-time operational decision making
▪ Quality = acting on poor quality before it occurs
▪ Cost monitoring = higher efficiency and effectiveness
▪ Performance metrics at individual nurse-patient encounter
Data Quality
▪ Structured data▪ Data entry/recopy errors▪ Programming errors▪ Work arounds (BCMA)▪ Event time vs. document time
▪ Unstructured data▪ Narrative hard to quantify▪ Natural Language Processing
(Siri?)▪ Pattern recognition (xray)▪ Expert systems
Real-Time Clinical and Operational Performance
Performance vs Outcomes
Missed Care Potential Quality/Safety Issues
Pain Management Pt Satisfaction; Increased LOS*
Administer meds on timePt Satisfaction; Increased LOS*; Clinical deterioration, e.g. renal effects from improper aminoglycoside admin
Prepare Pt/Family for discharge Readmission < 30d*
Adequate pt surveillance Infections; Clinical deterioration; Increased LOS*;
Oral hygiene Infections; Increased LOS*; Ventilator acquired pneumonia
Educating pts/families Readmission < 30d*
Comfort/talk w patients Pt satisfaction
Change patient position Pressure ulcers*
* Potential for increased cost of care
Quality Performance Metrics for Nursing
Unit/Hospital
▪ Infection rates
▪ Falls & injuries
▪ Pressure ulcers
▪ Patient level nursing costs and intensity
▪ Staffing and assignment
▪ Staff turnover, vacancy rates
Individual Nurse(s)
▪ Medication administration delays and omissions
▪ Pain assessment and management
▪ Other symptom management, e.g. hyper or hypoglycemia
▪ Patient progression (achieving nursing outcomes)▪ Mobility, activity▪ Nutrition▪ Respiratory/cardiac▪ Pain management
Clinical Performance Indicators
▪ Medication Administration
▪ Time delays and omissions
▪ 1 and 2 hour windows
▪ Critical medications, e.g. aminoglycoside antibiotics
▪ PRN medications
▪ Time
▪ Med Admin – Med Due
▪ Med Admin – Med pickup (Pyxis)
▪ Patterns
▪ PRN dose time and amount
▪ Delays and omissions
Medication Administration
Clinical Issues
▪ High risk drugs▪ Insulin, heparin▪ Aminoglycoside Antibiotics
▪ High volume drugs
▪ Pain control (PRN med usage)
▪ Delayed/omitted doses and hospital outcomes
▪ Medication administration volume and complexity
Operational Issues
▪ Patterns of delays & omissions▪ Relationship to workload ▪ Staffing vs. med admin complexity▪ Patterns and trends
▪ PRN practice patterns▪ Day/night shift▪ PRN opioid distribution▪ Relationship with patient satisfaction
▪ Performance▪ Unit level▪ Nurse level▪ Patient level
Hospital Medication Administration
Prescription• MD: Physician Order• CPOE
Dispensing• PharmD: 1. Drug Scheduling 2. Dispensing
• eMAR/Pyxis (or equivalent)
Administration• RN: Medication Administration • BCMA
Process vs. Performance in Med Admin
Prescription• Delayed Rx• Contraindicated• Drug-drug interaction• Polypharmacy• Allergy• Off label• Not standard of care• Inexperienced MD (resident)
Dispensing• Delayed dispensing• Scheduling conflicts• Wrong dose, route, time +• Label errors (cannot scan)• Wrong patient• Lack of drug (shortages, supply issues, surge use, etc.)• Inexperienced PharmD
Administration
• Delayed administration or omission• Multiple patients• Med admin complexity (stool softener vs intropic agent)• ↓PRN med admin (e.g. narcotic analgesics)• Practice variation• Equipment failure (BCMA eMAR)• Float/traveler nurse• Inexperience RN (new grad, float nurse)
Real-Time Medication Administration Analysis
▪ Due vs. admin time
▪ Delays and omissions
▪ Pyxis to BCMA – interruptions?
▪ PRN med patterns (pain management)
▪ Dose to dose variation (antibiotics)
▪ High alert drugs: insulin, anticoagulants, etc.
▪ Nurse – patient – unit analysis
Clinical Performance Indicators
Some Research Questions
▪ Do late/early doses of aminoglycoside antibiotics have direct clinical effects that influence outcomes of care?
▪ Are their practice differences among nurses in administering opioids for pain control?
▪ Is there a relationship between medication administration complexity and nurse workload?
▪ Are delays in administering medications related to high workload, high acuity shifts?
▪ Do long time between drug pickup (Pyxis) and administration identify potential interruptions in nurse workflow?
Future Directions
▪ Real-time information systems
▪ Comparison across different settings
▪ Follow “patient” across all encounters
▪ Link all providers to each patient, family, community
▪ Performance based analysis
▪ Share/compare data
▪ Value-driven healthcare
Common Data Model
▪ Patient focused
▪ Setting neutral
▪ Identifies nurse as provider of care
▪ Direct care hours and costs based on nurse-patient encounter
▪ Ability to directly bill for nursing care
▪ Problem/intervention/outcomes
Nurse_Patient_Encounter
PK ID_Encounter
FK3 ID_EpisodeFK2 ID_Nurse DayTime_Start DayTime_End Shift Type
Patient
PK ID_Patient
Age Race Sex OtherDemographics
Nurse
PK ID_Nurse
FK1 ID_Unit DOB Race Sex JobClass Date RN Date Hire Wage HighestDegree AssignedUnit FTE Agency NPI
PtLocation
PK ID_PtLocation
FK1 ID_EpisodeFK2 ID_Unit Unit_ID DayTime_Start DayTime_End Admit (y/n) Discharge (y/n)
Episode
PK ID_Episode
FK2 ID_Patient EpisodeType DateAdmit DateDischarged AdmissionSource DischargeDispition DRG APRDRG Payer ProcedureCode(1-15) Primary DX Secondary DX (2-15) Readm<30d
Outcomes
PK ID_Outcome
FK1 ID_EpisodeFK2 ID_FlowSheetData OutcomeDayTime OutcomeItem OutcomeScore
Intervention
PK ID_Intervention
FK1 ID_Episode InterventionDayTime InterventionCode InterventionClass
Nurse_Certifications
PK ID_Certification
FK1 ID_Nurse Certification Type DateStart DateExpire
Nurse_Credential
PK ID_Credential
FK1 ID_Nurse Credential_Type DateAwarded DateExpire
Unit
PK ID_Unit
UnitName UnitType NDNQI class Beds
Charges
PK,FK1 ID_Episode
FK2 ChargeID ChargeItem Units Charge
UnitBudget
PK ID_UnitBudget
FK1 ID_Unit BudgetPeriod RN_salaries RN_hours NurAide_hours NurAide_salaries Other_hours Other_salaries RN_FThires RN_FTterminate RN_BudgetedFTE NurAide_BudgetFTE TotalPatientDays
FlowSheetData
PK ID_FlowSheetData
FlowSheetDateTime ItemLabel ItemValue
Nursing Value Generic rev18 Nursing Common Data Model
ChargeMaster
PK ChargeID
Charge Description Charge
PtProblem
PK ID_PtProblem
FK1 ID_NurseFK2 ID_EpisodeFK3 ID_FlowSheetData ProblemIdentDateTime ProblemItem ProblemDesc ProbResolutionDate
CostItem
PK ID_CostItem
FK1 ID_UnitBudgetFK2 ID_EncounterCost TotalHours TotalCosts SumDirecCareCosts IndirectCareHours IndirectCareCosts IndirectCareCostAverage Benefit Costs
EncounterCost
PK ID_EncounterCost
FK1 ID_Encounter DirectCareHours DirectCareCost NurseWage ShiftDifferential OtherShiftCosts
ChargeCost
PK ChargeCost_ID
FK1 ChargeIDFK2 ID_CostItemFK3 ID_EncounterCost BudgetPeriod IndirectCareCostAverage PatientNursingCost
Green = costs; Blue = patient; Purple = nurse/provider; Red = facility/business entity
Nursing Management Minimum Data Set
Business Intelligence and Analytics
▪ Real-time clinical data▪ Sepsis algorithms▪ Care trajectory▪ Pain management
▪ Healthcare Business and Intelligence▪ Optimizing care delivery systems▪ Trending, forecasting, volatility
analysis, pattern recognition, etc.
▪ Outlier analysis▪ Adjust clinical care▪ Optimize outcomes
http://www.equest.com/wp-content/uploads/2013/08/dashboard-snockered-624x418.png
Quality Framework
▪ Traditional View
▪ Monitor/surveillance
▪ Root cause
▪ React to poor quality
▪ Nursing time and costs allocated as a department mean per patient day
▪ Future View
▪ Predictive models
▪ Multiple/interactive cause
▪ Predict and prevent
▪ Nursing time and costs allocated directly to each patient in real-time
Welton, J. M. (2008). Implications of Medicare reimbursement changes related to inpatient nursing care quality. Journal of Nursing Administration, 38, 325-330.
Nursing Value Work Group (7)
▪ Key consensus items▪ Nursing as a practice discipline ▪ Nurses as providers of care▪ Nursing measured at individual nurse-patient encounter▪ Need for common data model to extract relevant costs and
quality data▪ Patient level nursing costing model
PhD Student Core Competencies
Big Data Core Competencies
▪ Database theory and extraction methods
▪ Business intelligence and analytics
▪ Applying statistical techniques to real world problems
▪ Real-time data
Informatics Competencies
▪ Information systems, data storage, processing retrieval
▪ Performance using large data sets, e.g. genomics
▪ Developing common data models
▪ Nursing terminologies, representation of nursing and health care
▪ Natural language processing
▪ Data mining tool kit
What do you take away today?
• Better understanding how to use existing data (including cost data) to improve care
• Optimize clinical and operational environments of care
• Move towards a data-driven and value-based nursing practice model
• Provide the “best” nursing care at the highest quality and lowest cost (the value equation) = best outcome
• The value of nursing can only be described when the financial impact is included
Summary Points
Healthcare Costs
32
Grocery Store Problem
▪ How much do things cost?
▪ How much do you have to spend?
▪ What are bargains?
▪ What if there was no price?
▪ What if everything was the same price?
Cost of Care
▪ How much does care cost at your institution?
▪ What are costs, quality, and outcomes of INDIVIDUAL patients?
▪ How does YOUR hospital compare to other hospitals?
http://www.bcbsm.com/home/images/rising_cost/dollar_is_spent.gif
5.0 10.0 15.0
Routine Care Nursing Intensity
0
200
400
600
800
1,000
1,200
1,400F
req
uen
cy
Mean = 9.761Std. Dev. = 2.79N = 35,723
Variability of Nursing Time
Welton, J. M., Fischer, M., DeGrace, S., & Zone-Smith, L. (2006). Hospital nursing costs, billing, and reimbursement. Nursing Economics, 24, 239-245.
Welton, J. M., Unruh, L., & Halloran, E. J. (2006). Nurse staffing, nursing intensity, staff mix, and direct nursing care costs across Massachusetts hospitals. Journal of Nursing Administration, 36, 416-425.
Healthcare Utilization
▪ Supply vs. demand for healthcare services
▪ Roemer’s Law
▪ Over served vs. under served?
▪ Rural vs. urban
▪ Utilization of services by population
Gawande, A. (2009). The Cost Conundrum What a Texas town can teach us about health care. The New Yorker. http://www.newyorker.com/reporting/2009/06/01/090601fa_fact_gawande?printable=true
The Healthcare Price Problem
▪ Why do hospital charges vary so much?
▪ How much does it cost me to . . .
▪ Does competition increase costs to patients?
▪ Why is utilization higher in some parts of the country?
The Healthcare Price Problem
▪ Tylenol $1.50/pill (Amazon, $1.49/100 pills
▪ Gauze pads: $77 Walgreens “a few dollars”
▪ Troponin lab: $199.50 (Medicare $13.94)
▪ CBC lab: $157.61 (Medicare $11.02)
▪ Accu-Check diabetes test strips $18/each (Amazon $27/box of 50 = $0.55)
Medicare Provider Utilization and Payment Data
▪ Provider and claims based
▪ Fee for service
Homework/Project
Problem
▪ Limited access to primary care in rural CO
▪ Changing demographics (older population, providers moving from rural areas, etc.)
▪ Lack of specialty care
▪ No hospitals in the community
Analysis
▪ How many counties in CO do not have hospitals:http://www.unitedstateszipcodes.org/co/ (merge zip -> county data)?
▪ How many MD and APRN/PA providers are in each county ?
▪ What is the change in providers from 2012 to 2013 data?
▪ What are the top 10 procedures for each county?
▪ What are total billables for each county?
▪ What are total unique patients for each year 2012-2013?
Potential Solution
Community Paramedics
▪ EMS/Fire Department based
▪ Knowledge of community
▪ Mobile
▪ Technology capable, e.g. telehealth, point of care labs
More information?
▪ How many ambulance runs per county and per number of unique patients (see prior) 2012-2013
▪ How many ambulance runs in counties with no hospitals?
▪ Number of emergency visits and hospitalizations for each year by county (note some counties will not have hospitals)
▪ How many non emergency transports
Assignment
Services▪ ## hcpcs_description
▪ ## [1,] "Pathology examination of tissue using a microscope, intermediate complexity"
▪ ## [2,] "Ambulance service, basic life support, non-emergency transport, (bls)"
▪ ## [3,] "Emergency department visit, problem with significant threat to life or function"
▪ ## [4,] "Ambulance service, advanced life support, emergency transport, level 1 (als1-emergency)"
▪ ## [5,] "Subsequent hospital inpatient care, typically 35 minutes per day"
▪ ## [6,] "Initial hospital inpatient care, typically 70 minutes per day"
▪ ## [7,] "Removal of cataract with insertion of lens"
▪ ## [8,] "Subsequent hospital inpatient care, typically 25 minutes per day"
▪ ## [9,] "Established patient office or other outpatient visit, typically 15 minutes"
▪ ## [10,] "Established patient office or other outpatient, visit typically 25 minutes"
New variables
▪ Hospital inpatient care
▪ Ambulance Service
▪ Ambulance service, basic life support, non-emergency transport
▪ Number of non emergency transports in counties without hospitals?
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