Shahadat Uddin - University of Sydney, Complex Systems Research Centre - The use of patient data to explore healthcare coordination and collaboration

The use of patient data to explore healthcare coordination and

collaboration

Dr Shahadat Uddin Complex Systems Research Centre

Faculty of Engineering & IT

University of Sydney, Australia

Hospital Costing & Health Coding Forum Advancing Activity Based Management for quality & performance

Agenda

Establishing the research context: the use of patient data or electronic Medical Record

(eMR)

Propose a research framework for exploring healthcare coordination and collaboration

using patient data/ eMR

Illustrate the application of this proposed research framework.


collaboration

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collaboration

Research Context: trend in using eMR for scientific research

Figure: Publication trends of different types (e.g. journal and conference) over time using eMR as evident in Scopus

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collaboration

Research Context: trend in using eMR (cont.)

eMR is mainly stored for Administrative reason, mostly for billing purposes.

The advent of new technology and data analytics methods offer some further use of these

data for human well-beings

In Australia, some of eMR data are now accessible through collaborative research with public

(CHeReL) and private (e.g. Lorica) health organisations.

In recent times, eMR has mostly been used for exploring market-level research questions, e.g. Comparative cost analysis of alternative medical options (Lin et al., 2014)

Comparative effectiveness analysis of alternative medical options (Wang et al., 2013)

Usage pattern of a particular medical service (Chu et al., 2015)

Usage pattern of healthcare services by particular patient group (e.g. diabetic) (e.g. Stey et al. 2015).

Researchers also apply some sophisticated methods on eMR to explore research questions

related to longitudinal usages of medical services, such as – Agent-based models for identifying depression (Silverman et al., 2015)

Statistical probabilistic models for detecting the epidemic of diabetes (Hoffmann et al., 2012).

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collaboration

Research Context: trend in using eMR (cont.)

This study/presentation will take a network approach in exploring healthcare coordination and

collaboration among healthcare professionals using health insurance claim data/eMR. In

particular it will make the following contributions:

It will propose a research framework to explore healthcare coordination and collaboration

using patient data/eMR.

Using this proposed framework, it will then explore the following network among healthcare

professionals for examining patient outcomes

Patient-Centric Care Network (PCCN).

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collaboration

Healthcare Coordination

Coordination is defined as the harmonious functioning of parts (or members of a team) for

effective results (Malone, 1987)

In the growing literature of integrated delivery systems (e.g. hospital healthcare system),

coordination has been considered a central issue (Uddin and Hossain, 2014).

A Hip-replacement

patient Imaging and/or

Pathology

Hospital nursing

team

Hospital

Surgeons

Before surgery After surgery

Conduct surgery

A proper coordination among these three parties => Better patient outcomes

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collaboration

Healthcare Collaboration

Collaboration, which is a recurring process where two or more people or organisations work

together towards common goals (De Vreede et al., 2005).

There is a distinguished difference between coordination and collaboration in the sense that

for fruitful coordination harmonious functioning of parts are essential; whereas, prolific

collaboration demands joint works with others on a common goal that is beyond what any

single person or group can accomplish alone.

In the previous slide, a proper coordination is required among physicians, imaging

department and hospital nurse unit. However, they do not need to change their basic ways of

doing things.

On the other hand, the relation emerges between a nurse and a physician is a collaborative

relation since both of them required to work jointly in providing healthcare services to

patients.

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collaboration

Healthcare Collaboration (cont.)

To summarise

In coordination, participant actors are inter-dependent and need to work independently to

meet the requirement of the subsequent actor. The output of one stage will be the input of

the next stage.

Collaboration requires the participating actors to work in groups, otherwise the goal can not

be achieved.

A hospitalised

patient

Physicians Need to work

together/collaborate

Hospital nurses

Providing treatment

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collaboration

Information contained in eMR

Claims: medical claims, hospital claims and ancillary claims

Physician visit information

Detailed cost information for healthcare services: how much Physician visit or hospital service cost?

Length of stay

Infection, surgical complexity, readmission etc. from different flags of the claim submission form

Disease severity/ comorbidity from ICD

Membership information: age, locality etc.

Hospital information: hospital locality etc.

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A research framework to explore healthcare coordination and collaboration using eMR

Health Insurance Claim Dataset

Apply

Data extraction techniques (e.g.

retrieval of membership information

and analysis of claim data to trace

how many physicians provided

treatment to a specific patient)

Network formation concept (e.g.

patient-sharing network among

physicians for tracing physician

collaboration network)

Capture different networks among

healthcare professionals, such as, Patient-Centric Care Network

(PCCN)

Hospital-Rehab Coordination

Network (HRCN)

Referral Network among Specialist

Physicians (RCNSP) Physician Collaboration Network

(PCN)

Moderating factors, such as, Disease severity of patient and socio-

demographic information of patient,

physician and hospital

Social network methods and models,

such as, Network measures (e.g. centrality) Exponential random graph models Stochastic actor-based models

Statistical methods to check

significance and direction of

relations, such as, Correlation and Regression t-test and Chi-square test

Expected outcomes Extract network characteristics of

effective coordination and

collaboration networks and socio-

demographic features of their member

actors.

Develop predictive models To estimate different healthcare output

measures including- Hospitalisation cost Hospital length of stay Readmission rate; and Required number of physician visits

Membership

info

ICD for

disease

severity

Mostly medical

claims. Also use

ancillary and

hospital claims

By following

social network

theory

Patient-Centric Care Network

(PCCN)

Flags (claims):

infection,

complexity, etc.

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Application of the proposed research framework to explore

Patient-Centric Care Network (PCCN)

For a hospitalised patient, a network among physicians evolves over time

From one hospital to another hospital this network can differ significantly

This network also differs based on the type of disease that a patient has

This illustration of the proposed framework will analyse PCCN and answer the following

research question:

What are the structural and socio-metric attributes of PCCN affect patient

outcomes and how?

Multi-level analysis

for patients suffering

from same disease

Consider patient-centric

physician collaboration

networks

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Patient-Centric Care Network (PCCN) – cont.…

Membership

data and ICD

Need to construct

PCN from

medical claim

From statistics of

physician-visits

data from claims

info and form

Multi-level

regression

Classification criteria for patient-centric care network

● Physician community structure ● Network connectivity

Lev

el 1

L

evel

2

Independent variables

# Of different physicians

Average visit per physician

Dependent variables

Hospitalisation cost

Length of stay

Readmission

Moderating variables

● Age ● Gender ● Comorbidity score

A research model for exploring patient-centric care network

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Lev

el 1

L

evel

2




Dependent variables


Length of stay

Readmission




Capturing Physician networks - Physicians’ patient-sharing network or Patient-sharing network among physicians

Physician Patient

Ph1

Ph2

Ph3

Ph4

Pa1

Pa2

Pa3

Ph1

Ph4

Ph3

Ph2

1

1 1

2

2

(a) Patient-physician network (b) Corresponding PCN

Figure: Construction of physician network from patient-sharing network



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- 20 patients; visited by 85 physicians; and 169 times - Triangular yellow: patient - Circular red: physician

- 5 communities

- 17 physicians on average per community

Ph

ysic

ian

co

llab

ora

tio

n n

etw

ork



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Apply community

detection algorithm

(Amiri et al., 2004)



Data source

2352 patients (only hip replacement) in 85 different hospitals (i.e. 85 physician networks)

Number of physicians: 2229 (65871 medical claims)

Level 1 variables


— Number of different physicians

— Average visits per physician


— Age (69.09 years)

— Gender (male 1128 and female 1224)

— Comorbidity index (less than 5% patients): Charlson-Deyo index

Level 2 variables/Categorisation variable

Physician community structure (average number of physicians per community in a physician

network): 13.42

Network connectivity: 0.27 (27% of possible connections among physicians are present)

Dependent variables

— Hospitalisation cost ($31036)

— Length of stay (10.79 days)

— Readmission (approximately 15% of total patients)

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Statistical analysis approach



Lev

el 1

L

evel

2




Dependent variables


Length of stay

Readmission




3 dependent variables: need to develop 3 models

Hospitalisation cost and length of stay: multi-level multiple linear regression

Readmission: multi-level binomial logistic regression


Length of stay

Readmission

Multi-level

multiple linear

regression

Multi-level

binomial logistic

regression

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Findings

Average visit per physician shows significant impact on hospitalisation cost, length of stay

and readmission (level-1)

However, number of different physicians shows significant impact only for hospitalisation cost

and readmission (level-1)

Patient age moderates the relation between the two independent variables and

hospitalisation cost (level-1)

Patient gender and comorbidity index do not show any significant moderating impact (level-1)

The impact of independent and moderating variables on dependent variables show significant

variance across hospitals (i.e. patient-centric care networks) having different level of

community structure and network connectivity (level-2).



Level 1

Lev

el 2




Dependent variables


Length of stay

Readmission




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Summarised findings (cont.….)

Although some factors of PCCN have significant impact on healthcare outcomes, their impact

vary significantly across different hospitals with different physician network structures

This finding will help healthcare mangers in developing healthcare practice culture in their

respective organisations

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collaboration

Overall

This presentation proposed a research framework to explore various healthcare coordination

and collaboration networks

Employ this framework to Patient-Centric Care Network

This framework can also be employed to explore other healthcare networks

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Future and ongoing research

Hospital-Rehab patient journey

Incorporate Primary Health data (i.e. GP data)

Consider other hospital admission data (CHeReL from NSW Health)

Consider other socio-demographic parameters (e.g. Hospital types, sizes etc.)


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collaboration Table 3: Impact of independent and moderating variable on the dependent variables by considering first absence and then presence of grouping or clustering

variables of the second level.

(a) For hospitalization cost

Parameters

Hospitalization cost

Do not consider grouping

variables of Level-2

Consider Community

structure of Level-2

Consider Network

connectivity of Level-2

Estimate Significance Estimate Significance Estimate Significance

# of different physicians 5788.11 .000 5867.60 .000 5722.59 .000

Avg. visit per physician 3369.98 .000 3138.29 .000 3368.67 .000

# of different physicians*Age -31.88 .000 -32.98 .000 -31.87 .000

# of different physicians*Gender 283.51 .015 280.81 .016 300.18 .010

# of different physicians*Comorbidity score -1004.62 .036 -1075.95 .024 -1022.81 .032

Avg. visit per physician*Age -16.71 .055 -14.10 .107 -16.39 .060

Avg. visit per physician*Gender -329.99 .113 -309.47 .136 -359.03 .083

Avg. visit per physician*Comorbidity score 702.13 .389 805.39 .322 809.39 .376

(b) For length of stay

Parameters

Length of stay



Consider Community


Consider Network





# of different physicians*Age 0.01 .005 0.01 .003 0.01 .002

# of different physicians*Gender -0.10 .223 -0.10 .231 -0.10 .244

# of different physicians*Comorbidity score 0.26 .431 0.29 .391 0.28 .400

Avg. visit per physician*Age 0.01 .255 0.01 .332 0.01 .419


Avg. visit per physician*Comorbidity score -0.18 .750 -0.22 .704 -0.55 .719 21

Table 3: Impact of independent and moderating variable on the dependent variables by considering

first absence and then presence of grouping or clustering variables of the second level.

(a) For hospitalisation cost

Parameters

Hospitalization cost



Consider Community


Consider Network





# of different physicians*Age -31.88 .000 -32.98 .000 -31.87 .000

# of different physicians*Gender 283.51 .015 280.81 .016 300.18 .010

# of different physicians*Comorbidity score -1004.62 .036 -1075.95 .024 -1022.81 .032

Avg. visit per physician*Age -16.71 .055 -14.10 .107 -16.39 .060


Avg. visit per physician*Comorbidity score 702.13 .389 805.39 .322 809.39 .376


collaboration

Table 4: Different values of -2LL and their statistics for checking the impact of grouping variables

(i.e., Level-2 variables)

Dependent variable

(Level-1)

Group variable

(Level-2) -2LL Change of -2LL

(𝝌𝑪𝒉𝒂𝒏𝒈𝒆𝟐 )

Significancea

Hospitalization cost None 43338.85

11.20 p<0.01 Community structure 43327.65

Hospitalization cost None 43338.85

18.24 p<0.01 Network connectivity 43320.61

Length of stay None 14065.37

10.56 p<0.01 Community structure 14054.81

Length of stay None 14055.37

9.59 p<0.01 Network connectivity 14045.78

a. 𝜒𝐶ℎ𝑎𝑛𝑔𝑒2 = 6.63 at p=0.01 and df=1

Multilevel logistic regression using MLwiN software

http://www.methods.manchester.ac.uk/

http://blogs.helsinki.fi/methodology/chandola/

Centre for Research Methods, University of Manchester, UK

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http://blogs.helsinki.fi/methodology/chandola/

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3. Chu, C.-Y., W.-H. Lee, P.-C. Hsu, et al., TCTAP A-128 Impact of Regional Differences on Cardiovascular Outcome in Patients Undergoing Coronary

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S63-S64.

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6. Hoffmann F, Icks A. Diabetes' epidemic'in Germany? A critical look at health insurance data sources. Experimental and clinical endocrinology &

diabetes: official journal, German Society of Endocrinology [and] German Diabetes Association. 2012;120(7):410-5.

7. Malone T, editor What is coordination theory1988: Citeseer.

8. Uddin S, Hossain L. Social networks in exploring healthcare coordination. Asia Pacific Journal of Health Management 2014;9(3):53-62.

9. De Vreede GJ, Briggs RO. Collaboration engineering: designing repeatable processes for high-value collaborative tasks. HICSS '05 Proceedings of the

38th Annual Hawaii International Conference on Systems Sciences. 2005:17c-c.

10.Amiri B, Hossain L, Crawford JW, Wigand RT (2013) Community Detection in Complex Networks: Multi–objective Enhanced Firefly Algorithm.

Knowledge-Based Systems 46: 1-11.


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The use of patient data to explore healthcare

coordination and collaboration

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Health & Medicine

Shahadat Uddin - University of Sydney, Complex Systems Research Centre - The use of patient data to explore healthcare coordination and collaboration