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MM ANAGEMENTANAGEMENT M M ODELODEL HH EALTHCAREEALTHCARE F F ACILITIESACILITIES AA NN I I NTEGRATEDNTEGRATED

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A A N N I I NTEGRATED NTEGRATED

H H EALTHCARE EALTHCARE F F ACILITIES ACILITIES M M ANAGEMENT ANAGEMENT M M ODEL ODEL

Sarel Lavy and Dr. Igal M. Shohet

Technion – Israel Institute of Technology

Faculty of Civil and Environmental Engineering

(2)

OUTLINE

? Introduction

? Background

? Methodology

? Profile of facilities

? Conceptual model

(3)

INTRODUCTION

Business competition in healthcare

Requirements for economic operation

Higher efficiency demands

Developments in IT

Improved data processing capabilities

Artificial intelligence techniques

An Integrated Healthcare Facilities Management Model

(4)

BACKGROUND

The Pentagon of

Healthcare Facilities

Management core domains (Shohet and Lavy, 2004)

ICT

Healthcare Facilities Management

Maintenance Management

KPI’s, B enchmar king, Service life planning, C M

, PM, CBM

Perform ance M

anagem ent

Benchm arkin

g, Bu siness pe

rfor m ance, Build

ing ev aluation

techn iques

Risk M an

ag em

ent

Risk identification, A nalysis,

Quantification, Prioritisation, Value engineering, V

alue m anagem

ent

Development

Strategic planning, Reconstruction, Remodelling, Renovation, Rehabilitation

Su pply

Se rvic

es M ana

gem ent

Org an

isa tion

al lea

rn ing

, Ou tso

urc ing

, Cos

t-b en

efit

(5)

METHODOLOGY

? Structured field survey

? Statistical analysis

? Conceptual development of the model

? Computing development

? Validation

(6)

HEALTHCARE FACILITY CAMPUS

(7)

FIELD SURVEY

Profile of sample population of Israeli hospitals:

Number of facilities 12

Floor area (10

3

sq. m.) 83.4 (68.1)

Parking area (10

3

sq. m.) 25.6 (28.0)

Number of patient beds 669 (341)

Total ground area (10

3

sq. m.) 241.7 (235.1)

Occupancy (patient beds per 1,000 sq. m. floor area) 8.91 (1.90) In-house maintenance employees 47.6 (21.8) Patient beds per in-house maintenance employee 14.6 (3.3)

Parameter Mean (S.D.)

(8)

FIELD SURVEY

Average space distribution, by designation of floor area in Israeli public acute-care hospital facilities:

42.7%

10.7%

12.8%

9.1%

11.3%

6.8%

6.7%

Hospital wards Offices

Utilities Energy

Laboratories Clinics

Other

(9)

FIELD SURVEY

Building Performance Indicator (BPI)

Monitor the performance of Healthcare facilities based on Life Cycle Costs of the building systems (Shohet et al., 2003).

Interpretation:

BPI > 80 – Building at good or high service level;

70 ? BPI < 80 – Building at marginal condition;

60 ? BPI < 70 – Building at deteriorating condition;

BPI < 60 – Building at poor condition.

Each building system or component at performance level lower than 70 requires

preventive maintenance

(10)

FIELD SURVEY

Roofing element (Exterior envelope): Pn = 50

(11)

FIELD SURVEY

Electrical board (Electricity): Pn = 80

(12)

FIELD SURVEY

Performance scores of public acute care hospitals:

Building systems Mean (S.D.) Highest Lowest

Medical gases 87.5 (10.5) 100.0 71.4

Communication 82.2 (10.1) 96.0 70.1

Structure 79.9 (6.1) 91.3 70.6

Elevators 79.6 (5.7) 85.9 70.4

Electricity 77.8 (6.9) 88.2 65.5

HVAC 77.0 (7.1) 87.6 64.5

Fire protection 76.6 (14.2) 93.7 44.2

Interior finishes 76.2 (8.8) 90.0 63.1

Exterior envelope 74.4 (10.1) 94.1 59.0

Sanitary systems 69.9 (9.9) 89.2 57.1

Total BPI 76.6 (5.9) 85.5 67.8

(13)

FIELD SURVEY

Power consumption – Prediction model

W=3.23(FL)-23,228.7

? W – Annual water consumption [Cubic m./year]

? FL – Floor area [sq. m.]

? r

2

= 0.9680

Water consumption – Prediction model

E=22.66(OC)

2

-24,944(OC)+7,662(EM)+6,876,372

? E – Annual power consumption [KWH/year]

? OC – Occupancy [Number of total patient beds in the hospital]

? EM – Number of employees

? r

2

= 0.9748

(14)

CONCEPTUAL MODEL

Maintenance Performance &

Risk Management

Energy &

Operations Management Development

Models Knowledge Base

Maintenance Performance &

Risk Management

Energy &

Operations Management Development

Input

Reasoning Evaluator and Predictor

Output

(15)

CONCEPTUAL MODEL – INPUT

Definition of variables – Maintenance Management:

Geographic category (GEC); Type of facility (TOF);

Environmental conditions (ENV); Facility area (FAR);

Density (DEN); Occupancy (OCP); Actual annual

maintenance expenditure (AME); Actual maintenance resources (AMR); Availability of labor (AOL);

Facility designation (FAD); Actual service life (ASL);

Required service life (RSL).

(16)

CONCEPTUAL MODEL – INPUT

Definition of variables – Performance & Risk Management:

Physical performance (PPn);

Required performance (RPn);

Actual risk level (ARn);

Required risk level (RRn).

(17)

CONCEPTUAL MODEL – INPUT

Definition of variables – Energy & Operations Management:

Energy consumption (ECO);

Preparedness (PRE);

Actual annual operations expenditure (AOE).

(18)

CONCEPTUAL MODEL – OUTPUT

Maintenance Management:

Performance & Risk Management:

Energy & Operations Management:

Predicted annual mainetnance expenditure (PrAME);

Recommended maintenance policy (MPn).

Actual facility performance level (BPI);

Predicted risk level (PrRLn);

Predicted facility performance (PrFPn).

Predicted annual energy consumption (PrAEC);

Predicted annual operations expenditure (PrAOE).

(19)

Classify building/facility

Retrieve maintenance indicators

Predict maintenance activities and resources

Annual maintenance expenditure

Recommended maintenance policy Bank of performance

indicators

Case-Based Reasoning Input Interface

Reasoning Evaluator and Predictor

Output Interface Statistical models

INFERENCE MECHANISM

(20)

CONCEPTUAL MODEL – OVERVIEW

Occupants Requirements

RPn RRn PPn ARn

Facility Parameters

GEC TOF ENV FAD FAR DEN OCP RSL

CFn ASL

Performance & Risk PrRLn PrFPn

Maintenance PrAME MPn

Energy & Operation PrAEC PrAOC

Input Layer

Output

Layer

(21)

T T HANK HANK Y Y OU OU F F OR OR Y Y OUR OUR A A TTENTION TTENTION

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