Computers in Healthcare

Jinbo BiDepartment of Computer Science and Engineering

Connecticut Institute for Clinical and Translational ResearchUniversity of Connecticut

Presented at UConn Engr 1000 11 9th, 2012

HEALTHCARE – KNOWLEDGE OVERLOADED

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HEALTHCARE – DATA OVERLOADED

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COMPUTER SCIENCE TECHNIQUES – CRITICAL

Computing Techniques

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Known knowledge

Newly-published discoveries

MEDICAL INFORMATICS

becomes more and more important and indispensible due to Aging population Ever-increasing cost of delivering health care Outbreaks of emerging infectious diseases Availability and ubiquity of electronic health records Large quantity of clinical data consisting of

heterogeneous formats Many others

Let us look at 3 concrete examples … …

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APPLICATION 1: MEDICAL IMAGE INTERPRETATION

Challenges:– Massive image size– Time-consuming to analyze– Difficult to interpret

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CAD is an interdisciplinary technology combining elements of artificial intelligence and image processing with radiology

It provides doctors a “second opinion” for image interpretation

COMPUTER AIDED DIAGNOSIS (CAD)

CAD findings highlighted in red

CAD system

Radiology image input

radiologist

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LungCAD – DETECTING LUNG CANCER

/25LungCAD – a computer software product of Siemens Medical Solutions

SUCCESSFUL CAD SYSTEMSLungCAD: (FDA Approval) multi-center Multi-Reader Multi-

Case (MRMC) retrospective study to assess incremental value of LungCAD in assisting 17 general radiologists to detect pulmonary nodules

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Area under receiver operating curve with and without CAD for 17

readers

Average nonparametric ROC curve of all 17 readers with and without

CAD /25

TECHNIQUE SUMMARY

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Image processing

Machine learning

Computer vision

Algorithm complexity

Calculus Linear algebra

Computer programmin

gdatabases Data

structuresSoftware

Engineering

StatisticsMathematic

al programmin

g

Reliable

Data

Mortality?

Respiratory Compromis

e?Traumatic Brain

Injury?

Decision

Outputs

Major Hemorrhag

e?Real-Time Data

Processing

Integrated Decision

APPLICATION 2: TRAUMA PATIENT CARE

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TECHNIQUE SUMMARY

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Signal processing

Classification

Numerical analysis

Calculus Linear algebra

Computer programmin

gdatabases

Data structures & Algorithms

Software Engineering

Digital logic design

Computer Architecture

APPLICATION 3: QUALITY REPORTING

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Captures a provider’s compliance with accepted practices to improve the quality of care

Get providers prepared for “Pay for Performance”

Defined by external entities HQA, JCAHO, and CMS

Measures based on information traditionally stored in clinical notes

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CHART ABSTRACTION FOR QUALITY REPORTING

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Automatic Chart Abstraction

Manual Chart Abstraction

?Clin

ical

not

es,

com

pute

rized

pat

ient

dat

a

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PATIENT RECORDS

Patients – Criteria

Patient

1

428

diagnosis

250

AMI

2 414

3

250

429

SCIP

...

... ...

... ...

heart failure

diabetes

Code database

Look up ICD-9 codes

Patient – Notes

Patient

1

A

Note

B

C

D

E

2

F

G

...

... ...

... ...

Hospital Document DB Diagnostic Code DB

Statistics

reimbursement

Insurance

RWP/CC1 DICT. XXXXXXXXXXX P TRANS. XXXXXXXXXX P DOC.# 1554360 JOB # XXXXXXXXXX CC XXXXXXXXXX FILE CV XXXXXXXXXXXXXXXXXX. XXXXXXXXXXXXXXXXXX ORDXXXXXXX, XXXX L ADM DIAGNOSIS: BRADYCARDIA ANEMIA CHF ORD #: XXXXXXX DX XXXXXXX 14:10 PROCEDURE: CHEST - PA ` LATERAL ACCXXXXXX REPORT: CLINICAL HISTORY: CHEST PAIN. CHF. AP ERECT AND LATERAL VIEWS OF THE CHEST WERE OBTAINED. THERE ARE NO PRIOR STUDIES AVAILABLE FOR COMPARISON. THE TRACHEA IS NORMAL IN POSITION. HEART IS MODERATELY ENLARGED. HEMIDIAPHRAGMS ARE SMOOTH. THERE ARE SMALL BILATERAL PLEURAL EFFUSIONS. THERE IS ENGORGEMENT OF THE PULMONARY VASCULARITY. IMPRESSION: 1. CONGESTIVE HEART FAILURE WITH CARDIOMEGALY AND SMALL BILATERAL PLEURAL EFFUSIONS. 2. INCREASING OPACITY AT THE LEFT LUNG BASE LIKELY REPRESENTING PASSIVE ATELECTASIS. …. …………………. ……………. ……….

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Patients – Criteria

Patient

1

428

diagnosis

250

AMI

2 414

3

250

429

SCIP

...

... ...

... ...

heart failure

diabetes

Code database

Look up ICD-9 codes

Patient – Notes

Patient

1

A

Note

B

C

D

E

2

F

G

...

... ...

... ...

Hospital Document DB Diagnostic Code DB

Statistics

reimbursement

Insurance

RWP/CC1 DICT. XXXXXXXXXXX P TRANS. XXXXXXXXXX P DOC.# 1554360 JOB # XXXXXXXXXX CC XXXXXXXXXX FILE CV XXXXXXXXXXXXXXXXXX. XXXXXXXXXXXXXXXXXX ORDXXXXXXX, XXXX L ADM DIAGNOSIS: BRADYCARDIA ANEMIA CHF ORD #: XXXXXXX DX XXXXXXX 14:10 PROCEDURE: CHEST - PA ` LATERAL ACCXXXXXX REPORT: CLINICAL HISTORY: CHEST PAIN. CHF. AP ERECT AND LATERAL VIEWS OF THE CHEST WERE OBTAINED. THERE ARE NO PRIOR STUDIES AVAILABLE FOR COMPARISON. THE TRACHEA IS NORMAL IN POSITION. HEART IS MODERATELY ENLARGED. HEMIDIAPHRAGMS ARE SMOOTH. THERE ARE SMALL BILATERAL PLEURAL EFFUSIONS. THERE IS ENGORGEMENT OF THE PULMONARY VASCULARITY. IMPRESSION: 1. CONGESTIVE HEART FAILURE WITH CARDIOMEGALY AND SMALL BILATERAL PLEURAL EFFUSIONS. 2. INCREASING OPACITY AT THE LEFT LUNG BASE LIKELY REPRESENTING PASSIVE ATELECTASIS. …. …………………. ……………. ……….

FAMILY HISTORY: IS NONCONTRIBUTORY IN A PATIENT OF THIS AGE GROUP. SOCIAL HISTORY: SHE IS DIVORCED. THE PATIENT CURRENTLY LIVES AT BERKS HEIM. SHE IS ACCOMPANIED TODAY ON THIS VISIT BY HER DAUGHTER. SHE DOES NOT SMOKE OR ABUSE ALCOHOLIC BEVERAGES. PHYSICAL EXAMINATION: GENERAL: THIS IS AN ELDERLY, VERY PALE-APPEARING FEMALE WHO IS SITTING IN A WHEELCHAIR AND WAS EXAMINED IN HER WHEELCHAIR. HEENT: SHE IS WEARING GLASSES. SITTING UPRIGHT IN A WHEELCHAIR. NECK: NECK VEINS WERE NONDISTENDED. I COULD NOT HEAR A LOUD CAROTID BRUIT. LUNGS: HAVE DIMINISHED BREATH SOUNDS AT THE BASES WITH NO LOUD WHEEZES, RALES OR RHONCHI. HEART: HEART TONES WERE BRADYCARDIC, REGULAR AND RATHER DISTANT WITH A SYSTOLIC MURMUR HEARD AT THE LEFT LOWER STERNAL BORDER. I COULD NOT HEAR A LOUD GALLOP RHYTHM WITH HER SITTING UPRIGHT OR A LOUD DIASTOLIC MURMUR. ABDOMEN: WAS SOFT AND NONTENDER. EXTREMITIES: ARE REMARKABLE FOR THE FACT THAT SHE HAS A BRACE ON HER LEFT LOWER EXTREMITY. THERE DID NOT APPEAR TO BE SIGNIFICANT PERIPHERAL EDEMA. NEUROLOGIC: SHE CLEARLY HAD RESIDUAL HEMIPARESIS FROM HER PREVIOUS STROKE, BUT SHE WAS AWAKE AND ALERT AND ANSWERING QUESTIONS APPROPRIATELY. ……………… ……………….. ……….. ………… ……… …….. …….

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PATIENT RECORDS

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AUTOMATIC CHART ABSTRACTION

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VALIDATION OF OUR AUTOMATED APPROACH

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Databases from a hospital that deals with heart disease

Patient records of two quarters in 2005 A patient cohort of 325 patients based on billing

codes Automated system completely blinded to

manual results Performance compared on the agreement of

automatic results and manual reading results

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AGREEMENT BETWEEN MANUAL & AUTOAMTED

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Overall 96%

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QUALITY IMPROVEMENT

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Less than 30 Days

Automatic

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TECHNIQUE SUMMARY

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Natural Language Processing

Data miningMachine learning

Calculus Linear algebra

Computer programmin

gdatabases

Data structures & Algorithms

Software Engineering

StatisticsKnowledge

representation

0 20 40 60 80bpm

respiratory rate (RR)

hemorrhagecontrol

0

1000

2000

710 720 730 74060

80

100

time (s)

MANY MORE APPLICATYION AREAS

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Patient care management : trauma, diabetes, stroke, cancer, etc.

Genome linkage analysis to identify genes for cancer, substance dependence, cardiovascular disease

Therapy optimization Automatic meta-review Personalized medicine E-heatlhcare ….. …..

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New Era of Medical Informatics

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Thanks for your attendance

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