Understanding Waste for Lean Health Information Systems:

A Preliminary Review

Nadia Awang Kalong & Maryati Mohd. YusofNadia Awang Kalong & Maryati Mohd. Yusof

Strategic Information Systems GroupStrategic Information Systems GroupCentre for Software Technology & ManagementCentre for Software Technology & Management

Faculty of Information Science & Technology Faculty of Information Science & Technology Universiti Kebangsaan MalaysiaUniversiti Kebangsaan Malaysia

Outline

• Introduction• Waste • Method • Results• Discussion• Conclusion

Introduction

• Rising healthcare cost is due to service inefficiency that leads to huge medical errors

• HIS: enabler and barrier to service improvement• Need for holistic and systems thinking approach to

improve HIS effectiveness and efficiency Lean • identifying and eliminating waste

• Lack of waste understanding – barrier to applying Lean in supporting HIS improvement.

Introduction

• Limited Lean study in Health Information Systems (HIS) primarily in waste identification.

• Review the literature to provide an insight into the nature of waste in HIS from the perspective of Lean management.

Waste

• Refers to non-value added activity that exists in a process flow.

• Also defined as any item for which a customer refuses to pay.

• 7 categories of waste (manufacturing sector by Ohno): over-production, inventory, waiting, transportation, over-processing, motion and errors.

• No waste categories identified in studies in the HIS domain

Methods

• Electronic search: PubMed, Ebcohost, Science Direct, Scopus, ISI Web of Knowledge

• Search terms: Lean, waste, healthcare, informatics and information technology

• Key expert research, established textbooks, web search engines, and citation searching and chaining

Results

• Models that can be used directly to evaluate waste and enhance Lean transformation in HIS are limited.

• 8 research discussed waste models for the healthcare and IT domains– 4 in healthcare domain– 4 in IT domain

Results

• Waste in healthcare– Direct application– Define a new sub category – expensive input– Minor modification to Ohno’s; new category -

confusion• Waste in IT– Major modification; mapped new category with

Ohno’s– Identify irrelevancy and inconsistency to IT service

Results No. of

categoriesManufacturing Healthcare Information Technology

Ohno (1988) Bush (2007) Bentley et al. (2008)

Jimmerson (2009)

Berwick & Hackbarth (2012)

Poppendieck & Poppendieck

(2003)

Hicks (2007) Bell & Orzen (2010)

Kundu &Manohar (2011)

1 Transportation Transportation Inefficient processes

Motion/Conveyance

Handoffs Mass Electronic communication

Handoffs Transportation

2 Over-processing Processing Duplicate of services

Over-processing Relearning Failure demand Over-processing Over-processing

3 Over-production Over-production Over-production Over-treatment Extra features Flow excess Over-production

4 Inventory Stock on hand Inefficient processes

Inventory Partially done work Legacy databases and file archives

Inventory Inventory

5 Waiting Time on hand Inefficient processes

Waiting Delays Flow demand Waiting Waiting

6 Motion Movement Inefficient processes

Motion/

Conveyance

Task switching Gatekeepers/Single seat licenses

Unnecessary motion

Motion

7 Errors Making defective products

Errors Errors Defects Flawed flow Defects Errors

8 Expensive inputs Resource inefficiency

9 Confusion Failures in execution of care delivery

Processing inefficiency

10 Failures of care coordination

Lack of system discipline

11 Administrative complexity

12 Pricing failures

13 Fraud and abuse

14 Reinvention

15 External quality enforcement

16 Recurring incident

17 Ineffective communication

18 Over engineering

19 Environmental waste

20 Underutilized human potential

Discussion

• We reviewed 8 waste models in the context of the healthcare and IT domains.

• A total of 20 waste categories were summarized• 7 waste categories from the manufacturing sector exist

in both the healthcare and IT domains.• Most of the proposed additional categories were

actually covered in the existing Ohno’s model, except– Environmental waste

• Thus, the original waste model can be adapted to identify waste in both the healthcare and IT sectors.

Conclusion

• Waste understanding in the IS context could contribute to a successful Lean transformation and improve HIS, but

• Studies related to Lean waste identification in HIS are still limited, maybe due to

• The nature of waste in the IS context is not clear and visible, unlike in the manufacturing domain.

• Ohno’s waste model is suitable to be used as a guideline in understanding and identifying waste in both the healthcare and IT domains and subsequently for improving HIS.

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Thank You!

Maryati M. Yusofmmy@ftsm.ukm.my