Transcript Clinical Informatics - Computational Bioscience Program

Clinical
Informatics
John Welton, PhD, RN, FAAN
CU College of Nursing
BIOS 6660
University of Colorado
College of Nursing
November 3, 2015
Big Data Concepts
Data Explosion
https://www.intelethernet-dell.com/wp-content/uploads/2011/09/Screen-shot2011-10-05-at-1.50.14-PM1.png
Challenges
▪ Storage, processing,
computational limitations
▪ Security, confidentiality, privacy
▪ Obsolescence of current
technology
http://oldcomputers.net/pics/osborne1.jpg
▪ Accessing data across multiple
settings
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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
http://d1mpb3f4gq7nrb.cloudfront.net/img/toons/cartoon6517.png
Items and Issues
▪ Data accuracy and missing data
▪ Structured vs. unstructured data
▪ Extraction and common data
models
▪ Lack of common data model
▪ Archiving and persistence of data
▪ Data consistency across time and
settings
▪ Version control, obsolescence
▪ 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
Operational/Organizational Focus
▪ Improve health/nursing care
▪ Healthcare workforce
▪ Optimize outcomes
▪ Resource utilization
▪ Population management
▪ Better patient experience
▪ 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
Operational
▪ Real-time clinical decision
making
▪ Real-time operational decision
making
▪ AI potential for pattern
recognition
▪ Quality = acting on poor quality
before it occurs
▪ Mapping trajectories of care
▪ Cost monitoring = higher
efficiency and effectiveness
▪ Acuity trending (patient, unit,
hospital/agency)
▪ Performance metrics at
individual nurse-patient
encounter
Data Quality
▪ Structured data
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Data entry/recopy errors
Programming errors
Work arounds (BCMA)
Event time vs. document time
▪ Unstructured data
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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 time
Pt 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
Individual Nurse(s)
▪ Infection rates
▪ Medication administration delays
and omissions
▪ Falls & injuries
▪ Pressure ulcers
▪ Patient level nursing costs and
intensity
▪ Staffing and assignment
▪ Staff turnover, vacancy rates
▪ Pain assessment and management
▪ Other symptom management, e.g.
hyper or hypoglycemia
▪ Patient progression (achieving
nursing outcomes)
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▪
▪
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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
Operational Issues
▪ High risk drugs
▪ Patterns of delays & omissions
▪ Insulin, heparin
▪ Aminoglycoside Antibiotics
▪ High volume drugs
▪ Pain control (PRN med usage)
▪ Delayed/omitted doses and
hospital outcomes
▪ Medication administration
volume and complexity
▪ 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
Green = costs; Blue = patient; Purple = nurse/provider; Red = facility/business entity
Nursing Value Generic rev18
Nursing Common Data Model
PK
▪ Patient focused
▪ Setting neutral
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
ID_Episode
FK2
ChargeID
ChargeItem
Units
Charge
PK
ID_Encounter
PK
ID_Credential
FK3
FK2
ID_Episode
ID_Nurse
DayTime_Start
DayTime_End
Shift
Type
FK1
ID_Nurse
Credential_Type
DateAwarded
DateExpire
PtProblem
Nurse
FlowSheetData
PK
ID_PtProblem
FK1
FK2
FK3
ID_Nurse
ID_Episode
ID_FlowSheetData
ProblemIdentDateTime
ProblemItem
ProblemDesc
ProbResolutionDate
PK
ID_Intervention
FK1
ID_Episode
InterventionDayTime
InterventionCode
InterventionClass
PK
Intervention
FK1
FK2
ID_Nurse
FK1
ID_Unit
DOB
Race
Sex
JobClass
Date RN
Date Hire
Wage
HighestDegree
AssignedUnit
FTE
Agency
NPI
EncounterCost
PK
ID_EncounterCost
FK1
ID_Encounter
DirectCareHours
DirectCareCost
NurseWage
ShiftDifferential
OtherShiftCosts
Outcomes
PK
PK
ID_FlowSheetData
FlowSheetDateTime
ItemLabel
ItemValue
Charges
PK,FK1
Nurse_Credential
Nurse_Patient_Encounter
ID_Patient
Age
Race
Sex
OtherDemographics
▪ Identifies nurse as provider of
care
▪ Direct care hours and costs
based on nurse-patient
encounter
Patient
ID_Outcome
ID_Episode
ID_FlowSheetData
OutcomeDayTime
OutcomeItem
OutcomeScore
Nurse_Certifications
PK
ID_Certification
FK1
ID_Nurse
Certification Type
DateStart
DateExpire
ChargeMaster
▪ Ability to directly bill for nursing
care
▪ Problem/intervention/outcomes
PK
UnitBudget
ChargeID
PtLocation
Charge Description
Charge
PK
ID_PtLocation
FK1
FK2
ID_Episode
ID_Unit
Unit_ID
DayTime_Start
DayTime_End
Admit (y/n)
Discharge (y/n)
ChargeCost
PK
ChargeCost_ID
FK1
FK2
FK3
ChargeID
ID_CostItem
ID_EncounterCost
BudgetPeriod
IndirectCareCostAverage
PatientNursingCost
PK
Unit
PK
ID_UnitBudget
ID_Unit
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
UnitName
UnitType
NDNQI class
Beds
CostItem
PK
ID_CostItem
FK1
FK2
ID_UnitBudget
ID_EncounterCost
TotalHours
TotalCosts
SumDirecCareCosts
IndirectCareHours
IndirectCareCosts
IndirectCareCostAverage
Benefit Costs
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
▪ Future View
▪ Monitor/surveillance
▪ Predictive models
▪ Root cause
▪ Multiple/interactive cause
▪ React to poor quality
▪ Predict and prevent
▪ Nursing time and costs allocated
as a department mean per
patient day
▪ Nursing time and costs allocated
directly to each patient in realtime
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
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PhD Student Core Competencies
Big Data Core Competencies
Informatics Competencies
▪ Database theory and extraction
methods
▪ Information systems, data storage,
processing retrieval
▪ Business intelligence and analytics
▪ Performance using large data sets,
e.g. genomics
▪ Applying statistical techniques to
real world problems
▪ Real-time data
▪ Developing common data models
▪ Nursing terminologies,
representation of nursing and health
care
▪ Natural language processing
▪ Data mining tool kit
Summary Points
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
Healthcare Costs
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?
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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
Variability of Nursing Time
1,400
1,200
Frequency
1,000
800
600
400
200
Mean = 9.761
Std. Dev. = 2.79
N = 35,723
0
5.0
10.0
15.0
Routine Care Nursing Intensity
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
Analysis
▪ Limited access to primary care in
rural CO
▪ How many counties in CO do not have
hospitals:
http://www.unitedstateszipcodes.org/co/
(merge zip -> county data)?
▪ Changing demographics (older
population, providers moving
from rural areas, etc.)
▪ How many MD and APRN/PA providers are in
each county ?
▪ Lack of specialty care
▪ What are the top 10 procedures for each
county?
▪ No hospitals in the community
▪ What are total billables for each county?
▪ What is the change in providers from 2012 to
2013 data?
▪ What are total unique patients for each year
2012-2013?
Potential Solution
Community Paramedics
More information?
▪ EMS/Fire Department based
▪ Knowledge of community
▪ How many ambulance runs per
county and per number of unique
patients (see prior) 2012-2013
▪ Mobile
▪ How many ambulance runs in
counties with no hospitals?
▪ Technology capable, e.g.
telehealth, point of care labs
▪ 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 (als1emergency)"
▪
## [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?