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The Many Lives of Data
James H. Willig, MD, MSPHAssociate Professor, Dpt of Medicine
Division of Infectious Disease University of Alabama at Birmingham
Outline for today
The first life – Clinical Intermezzo
The second life – Research Intermezzo
The third life – Enterprise Intermezzo
The value of data
Data Life #1: In the clinical setting
Document in the electronic medical record Some free text: HPI, A&P Some quantifiable fields: Exam, S&S Updates: Medication list, diagnoses/problem list Other sources: VS, laboratories, imaging, pathology, etc.
Good documentation Assists other allied health professionals (Social services, RN,
etc.) Assists other providers: Other specialists, hospital providers, etc. Informs appropriate billing
Data Life #1: In the clinical setting
Accuracy on which we all depend My errors = your problem
Patient safety Failing to document an allergy or an adverse event…. So much for your fancy clinical decision support algorithm… New data published – medication combination no longer safe….
Shared record = shared responsibilities How much time do we talk about data quality?
Documentation of Diagnosis; Accuracy1: 53 to 78%
0
10
20
30
40
50
60
70
80
4 to 5/ 06 6 to 7/ 06 8 to 9/06 10 to 11/06 12/ 06 to 1/ 07
1Accuracy % = Made/Mentioned (changes)
Documentation of medications; Accuracy1: 88-93%
85
86
87
88
89
90
91
92
93
4 to 5/ 06 6 to 7/ 06 8 to 9/06 10 to 11/06 12/ 06 to 1/ 07
1Accuracy % = Made/Mentioned (changes)
Documentation Errors – Medications*
OR 95% CITotal Changes 1.01 0.93 – 1.1
Experience< 6 mo vs > 6 mo
1.6 1.01 – 2.5
Sick call Y vs N 1.6 0.7 – 3.3
Attending vs NP 4.3 2.0 – 9.4
Fellow vs NP 2.5 1.6 – 3.8
*n = 2,078 observations
Data Life #1: In the clinical setting
If you are at all involved in patient care, the first life of data is central to your practice
Shared responsibility for accurate documentation Decisions are only as good as the data they are based on High data quality helps your patient receive better care/service in
your office and throughout the health system My poor documentation may hurt YOUR patient and vice versa High quality data allows you to rapidly ID patients in need of
changed therapy
Take pride in your documentation; Be > copy/paste!
Information integrity – Data quality Establish and maintain a culture of data quality
Add to training! Look for opportunities to provide feedback We do this! Critical review of outside or inpatient records.
Electronic documentation does not uniformly equal quality documentation Local quality as well as centralized quality both have roles (best
sources, best practices, common patterns of errors, etc.) Every dataset is perfect! Until you analyze it…
Systems to gauge data quality need to be in place Others have found solutions - Old concept other industries Break incremental mold, seek interdisciplinary junctions
Intermezzo: What’s the ‘so what’?
40% discharged pts have pending studies neither outpt providers or pts aware of despite requiring action
1 in 5 pts discharged from hospital will suffer an adverse event related to medical management within 3 wks 66% related to medications
Medication discrepancies outpt vs. prescribed at discharge 14% elderly pts with this are rehospitalized within 14 days vs. 6%
without discrepancies When med rec led by pharmacists medication related AEs in 30
days 1% vs. 11% in controls
Closer to home…
Chronic SC pain pump not documented
Admitted for renal failure To MICU (2 days) To floor, teams change next day
Patient complains of pain, PO opiates started Met call Subsequent aspiration pneumonia
Outline for today
The first life – Clinical Intermezzo
The second life – Research Intermezzo
The third life – Enterprise Intermezzo
The value of data
Data Life #2: Research
In aggregate, your data can be transformed into information
Individual documentation, done in a systematic fashion We all agree on template beforehand We all respect common documentation
Individual level documentation, unlocks population level insights!
1988
Demographic
Therapeutic
Concurrent Treatments
Clinical – HIV/AIDS events
Clinical – Comorbidities
Laboratory – HIV associated
Using REPO# and link to basicDocumentation:
• Collaborative basic science
• Disease pathogenesis
• Laboratory parameters
1988 1995Demographic
Therapeutic
Concurrent Treatments
Clinical – HIV/AIDS events
Clinical – Comorbidities
Laboratory – HIV associated
Laboratory – General
Socioeconomic
Health services utilization
Adherence – Self report
• Data collection is manual
• Simple software
• Data accumulating
• Processes established
• MS Access DB started
1988 1995 1999
Demographic
Therapeutic
Concurrent Treatments
Clinical – HIV/AIDS events
Clinical – Comorbidities
Laboratory – HIV associated
Laboratory – General
Socioeconomic
Health services utilization
Adherence – Self report
1988 1995 1999 2004Demographic
Therapeutic
Concurrent Treatments
Clinical – HIV/AIDS events
Clinical – Comorbidities
Laboratory – HIV associated
Laboratory – General
Socioeconomic
Health services utilization
Adherence – Self report
1988 1995 1999 2004 2008
Demographic
Therapeutic
Concurrent Treatments
Clinical – HIV/AIDS events
Clinical – Comorbidities
Laboratory – HIV associated
Laboratory – General
Socioeconomic
Health services utilization
Adherence – Self report
Patient Reported Outcomes
Resistance Data
Interval cohort era
Clinic cohort era begins – 1917 Clinic CPR
Data Life#2: Research 1917 Clinic 2008-2012 Processes put in place to access clinic data
Pace of local research accelerates● 2000-2006: 2.85 manuscripts published per year● 2007-2012: 15.66 manuscripts published per year
Large numbers of new grants and collaborators
New data types added Resistance data, patient reported outcomes data
New data type added 2008: Patient Reported Outcomes
The Medical Record is a relational database!
Innovation Area
Innovation area =
DT x MEDa
ta Ty
pes (
DT)
Methodological Expertise (ME)
Factors associated with 30 day readmission in patients with CHF?
LR
PH
Descriptive
DxLabsDemo Admit Co- morbid
OR
Meds
Demo
Labs
Meds
Dx
Co- morbid
LogisticRegression Admit
The Three Stages and Six Steps of Quantitative Analysis
Keeping up with the quants: your guide to understanding and using analytics. Thomas H. Davenport and Jinho Kim. Harvard business review press 2013.
Logistic Regression model: Outcome is self-reported SI – Yes.1
Unadjusted Adjusted
Age (per 10 years) 0.81 (0.69-0.96) 0.74 (0.58-0.96)
Depression (PHQ9) No Depression (0-4) Mild (5-9) Moderate (10-14) Mod/Severe (15-19) Severe (≥20) Unknown
0.06 (0.02-0.16)1.03.89 (2.16-7.02)9.16 (4.85-17.31)21.70 (11.37-41.43)2.12 (0.23-19.86)
0.08 (0.03-0.21)1.03.91 (2.12-7.22)9.08 (4.67-17.63)25.55 (12.73-51.30)2.05 (0.20-21.64)
Substance Abuse Never Yes – Historical Yes – Current
1.02.60 (1.73-3.90)6.32 (4.06-9.82)
1.01.15 (0.66-1.98)1.88 (1.03-3.44)
1. Model also adjusted for: Gender, race, insurance, location, CD4, alcohol use.2. Published in CID April 2010
PRO data
Advantages Decreased social desirability bias enhances quality of data
captured for sensitive domains Buying data directly from the manufacturer? (Adjunct?) Patient updates status of chronic diagnoses – now rather than
“Yes or No” from problem list, “current, prior, never” Clinical benefits (gain time, layer systems to enhance care) –
implementation into existing workflow is paramount
All research endeavors benefit from new data type
Today… The 1917 Clinic is an international leader in several lines
of research
The 1917 Clinic is a flexible “research platform” where investigators can bring their ideas, run them through our processes and gain quality data to power analyses
Note Growth of a research enterprise tied to data at every step A vision towards growth of data powers continued expansion and
enable clinic to become relevant in a “multicohort world”
How to remain relevant in a multicohort world? Research area = DT x ME
DT = Data types ME = methodological expertise (innovation)
Agility Cruise ships vs. speed boats Don’t get into a pushing contest with a wall
Search out the edges Interdisciplinary interstitium
Outline for today
The first life – Clinical Intermezzo
The second life – Research Intermezzo
The third life – Enterprise Intermezzo
The value of data
Data Life #3: Enterpise
Enterprise Data Warehouse
POWERINSIGHT
EDUCATION
RESEARCH
PATIENT CAREData
Aggregation
Data Access
DataExploration
Basics: Aims
Hospital/Clinic
Admission Date
MedicalRecordNumber(MRN)
Patient encounter, for a specific admission date, and a specific clinic
Basics: Slice and Dice
Data Life #3: Enterpise
PowerInsight Cerner product Provides a clinical data warehouse
Let’s take a look at some operational questions answered with PowerInsight
37
Clinical Operational Financial
Neurology Patients Tracking
New visits report by various dimensions
Revenue per admission by facility
Diabetic patients with HgA1C > 7
Order sets used by facility and physician
Total charges by payer
Turnaround time for ECG completion
Patients came through ER
Revenue per outpatient per facility
Inpatient admissions by nursing unit
Time between admit order and admission
Admission per health plan organization
Alerts overridden analysis Pastoral care referrals and consults
Revenue summary by health plan
Turnaround time for door to aspirin
Discharges by encounter -summary
Gross revenue by age group, facility, county.
Sample Questions
Why neurology patients end up in off-service beds?
• Is there an association between the time a discharge is entered and the time when a patient
is discharged?
Showcase: Neurology
Showcase: Neurology
Showcase: Neurology
What was the hourly traffic of patients arriving in the ED during a specific month?
• Goal: A need to better plan and allocate resources to better serve incoming patients.
Showcase: Emergency Dept.
Showcase: Emergency Dept.
Research
CSV
Excel
POWERINSIGHTDATA
Dashboard
Report
Interactive
Custom
Ad Hoc
Oper’tnl Strategic Analytic
PowerInsight = DATA
The Three Stages and Six Steps of Quantitative Analysis
Keeping up with the quants: your guide to understanding and using analytics. Thomas H. Davenport and Jinho Kim. Harvard business review press 2013.
“Business” Intelligence?
Maybe better titled “Clinical Business Intelligence”
What does that mean to us?• Measuring efficiency • Measuring clinical effectiveness• Measuring safety and quality• Measuring compliance
Outline for today
The first life – Clinical Intermezzo:
The second life – Research Intermezzo:
The third life – Enterprise Intermezzo:
The value of data
How do I play?
The value of data Data are data
Questions are questions
Economists call data a “non-rivalrous” good One person’s use of it does not impede another’s Data can be re-used limitlessly – how much can the information
produced be worth?
Facebook’s worth in 2011 prior to IPO Under accounting standards (equipment, physical assetts) $6.3 B Initial market value $ 104 B. Intangible assets make up gap User’s worth of data estimated at $100. Pt data worth?
Examples Wallmart, old sales receipts, hurricanes and PopTarts
“Target,” unscented lotions and reproduction
Value of our misspelling Microsoft Word approach ($$, cross referencing to databases,
etc.). Google approach (did you mean “x”? User teaches system correct spelling)
Credit card companies, others… Data on purchase patterns more important than commissions on
purchases
Three ways to unlock value: Reuse of data Search terms and google flu trends
Outpaces CDC by 1 week, spread to other conditions
Giant AOL hires amazon to handle technology end e-commerce business Seemed like outsourcing, Was data mining to improve
recommendation engine.
Security cameras in stores Now gauging traffic to reorganize store layout, gauge efficacy of
advertising campaigns
Three ways to unlock value: Recombinant Data Do cellphones increase cancer risk? (Thankfully no!)
Danish Cancer Society links nationwide cancer registry to commercial data on all cellphone subscribers since 1987
Hospital readmissions Finding of high prevalence of depression led to expansion of
mental health services
Remember the Innovation Area Data’s true power is unlocked when linked Unexpected correlations await if we can only analyze them…
Three ways to unlock value: Build extensibly Bringing a new tool into our milieu adds value, but
adding a new tool into our network more so
Consider secondary uses of new data source at the outset Google street view cars: capture GPS data, confirm map
information, took pictures of houses and roads Multiple secondary uses from these data streams ongoing
The Three Stages and Six Steps of Quantitative Analysis
Keeping up with the quants: your guide to understanding and using analytics. Thomas H. Davenport and Jinho Kim. Harvard business review press 2013.
Big data mindset: You are all data scientists Each of you have domain expertise
Think critically on what to “datafy” to add competive advantage Work in teams and support data holders (IT), and data specialists
(statisticians) to bring new insights forward
Remember: “What we are is merely a steppingstone to what we may become1”
1. Deus Ex: Human Revolution, 2012
Framing the problem
Solving the problem
Communicating and acting on result
Equation for getting up in the morning – what’s yours?
X= Patient Care
R = Research I = Informatics
E = Education
XRIE= Lives positively influenced