Improving End of Life Care: an Information Systems ... - ICMCC

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Improving End of Life Care: an Information Systems ... - ICMCC

Improving End of Life Care: an

Information Systems Approach

to Reducing Medical Errors

Tamang S. a,b , Kopec D. a , Shagas G .a , Levy K. a

a

Brooklyn College, City University of New York

b

Metropolitan Jewish Health System, Brooklyn, NY


Improving Health Care with

IS

• Quality Improvement: structure, process,

outcomes

• Medical errors: patients with chronic and

terminal illness

• Healthcare information system:

• Must be HIPPA compliant

• web-enabled enabled platforms

• open database connectivity

• warehousing

• electronic health records

• AI –diagnosis, prognosis, screening, tutoring

systems, data mining


CONFER

• Confer is an “eCare” solution developed by

MJHS IS Dept and Research Dept intern (3yrs

and ongoing)

• Accessed by managers and clinicians on

company Extranet

• Process-based application for care

management

• XML / XSL

• TransactSQL database

• Crystal report editor– stored SQL procedures


Identified program outcome to reduce emergent, unplanned care -

Hospital Log data– who, what, when, why, complications?


Hospital

Log

Demographics

Database Relationship Table

(data transformation to MSAccess format)


CONFER QOL Development

History

• Demographics & Action Log

• Performance measurement

• Human resource intensity

• Referral Log

• Internal and external referral of patients

to health and social services

• Hospital Log

• Emergent, unplanned care utilization

• complications


CONFER QOL Development

History

• Currently testing a Care Plan

Care goals

• Suggested interventions

• Services patient is currently receiving

• Reassessment / care planning

• Developing a Visit Log and Notes Log

for electronic charting –goal to move

data collection closer to the POINT

OF CARE


Demographics


Demographics


Referral Log


Hospital Log


Crystal Report Viewer


Beyond Data Management

• Intelligent palliative care information

systems – AI techniques (i.e. case-based

reasoning)

• Disseminating expert-level knowledge in

the field

• Supporting evidence-based practices

Improving the quality of healthcare

delivery

• Targeting vulnerable populations


Characteristics of the CBR

Cycle

• Recognize-act cycle

• The use of domain specific knowledge

• Knowledge representation (flex

modification of knowledge base)

• Expert lines of reasoning

• Explanation of the reasoning process

• Inter-disciplinary knowledge in solving a

problem


CSAPIAN CBR Shell 1

• The main blocks are:

• The case definition block, which defines the

structure of the case

• The repair rules definition block, which

defines the rules to adapt cases

• The modification definition block, which

defines rules which guide the matching

process

• The case base or the case library

1. Price CJ, Pegler IS. Deciding Parameter Values with case-Based

Reasoning. 1 st UK Workshop on Case-Based Reasoning. January 1995.


CAREN - CBR prototype

• “CAREN” CBR prototype – Master’s s thesis project

• Uses CASPIAN CBR Shell

• Study sample – palliative care patients in the

cancer trajectory

• Chart extraction / data collection for 17 patients

• Assignment of weights and indices

• Computing similarity – Nearest neighbor

matching


case instance patient_178 is

Test case 178

prim_diagnosis = CA;

sec_condition = HTN;

gender = female;

pain_sympt

= yes;

income = 2.00000; ~patient in

income group 2

adv_care = no;

lives_alone = yes;

visual = no;

speech = no;

Hearing = no;

dyspnea = no;

oxygen = no;

Hearing = no;

dyspnea = no;

oxygen = no;

cpap = no;

weightchange = yes;

insulin = no;

pu = no;

adl = yes;

behavior = no;

other = yes; ~patient

experiences other disease related

symptoms

hospice = no;


Indices

index definition is

index on prim_diagnosis;

index on lives_alone;

index on hospice;

index on adl;

index on pain_sympt

sympt;

index on gender;

end;

~primary diagnosis

~living status

~hospice status

~activities of

daily living

~pain symptoms

~gender


Case Matching and

Modification

(generated by the pre-processing processing rule repair definition)

** NEW PATIENT IS IN LOW INCOME GROUP

goal = research entitlements and coverage

goal = research cancer relief funds

** NEW PATIENT HAS NO ADV DIRECTIVES

goal = communicate with pt about preferences

goal = facilitate advances care planning

** ASSESS PATIENT LIVING ENVIRONMENT

goal = ensure safety in the patient home

goal = evaluate for durable medical goods


solution is

bio = [ 'Female - Cancer - Lives Alone - ADL' ];

goal1 = psychosocial_support;

end;

goal1_int1 = [ 'individual support therapy from SW- RN' ];

goal1_int2 = [ 'telephone check-in support' ];

gaol1_int3 = [ 'assist with ADLs' ];

goal1_int4 = [ 'coordinate access to social services' ];

goal2 = monitor_health_status;

goal2_int1 = [ 'program assessment' ];

goal2_int2 = [ 'contact with PCP' ];

goal3 = pain_management;

goal3_int1 = [ 'medication review' ];

goal3_int2 = [ 'consult with PCP about meds' ];


Findings and Results

• CONFER, MIS

• Track patients / collect and manage data

• Performance measurement and health care

utilization reporting component

• Moving data collection closer to the point of

care (electronic charting, field use)


Findings and Results

• CAREN, Case-based reasoning system for

palliative care consultation

• Identifies expert level care goals and intervention

strategies

• Rules can be used to enforce best practices for pain

management, advanced care planning, and patient

safety

• However, not as comprehensive as the experts –

important issues were missed by the CBR & are

relevant to the test case (depression)

• May be useful to train, or introduce palliative care

concepts to novices


Future Directions

• Applying CBR for Palliative Care

consultation

• Creating a web-based based CBR engine

integrated with an electronic health record

• Securely transferring information using

SFTP

• JAVA CBR engine


The end

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