Memorial Hospital Case Study

Introduction

Memorial Hospital is a privately owned 600-bed facility. The hospital provides a broad range of health care services, including complete laboratory and X-ray facilities, an emergency room, an intensive care unit, a cardiac care unit, and a psychiatric ward. Most of these services are provided by several other hospitals in the metropolitan area.

However, the hospital’s administrator, Janice Fry, is concerned about whether the hospital can really deliver on its promises, and worries that failure to provide the level of health care patients expect could drive patients away. Janice met recently with the hospital’s managerial personnel to discuss her concerns. 

Quality Assurance

Quality assurance is a discipline focused on ensuring that data is fit for use in business processes ranging from core operations to analytics and decision-making, regulatory compliance, and engagement and interaction with external entities.

As a discipline, it comprises much more than technology — it also includes roles and organizational structures, processes for monitoring, measuring, reporting and remediating data quality issues, and links to broader information governance activities via data-quality-specific policies (Attaran, 2001).

Given the scale and complexity of the data landscape across organizations of all sizes and in all industries, tools to help automate key elements of the discipline continue to attract more interest and to grow in value. As such, the data quality tools market continues to show substantial growth, while exhibiting innovation and change.Several other questions were asked concerning the hospital’s efforts to keep costs down. Some people were concerned that an emphasis on costs would be detrimental to quality. They argued that when a person’s life is at stake, costs should not be of concern (Troy, 2012).

The data quality tools market includes vendors that offer stand-alone software products to address the core functional requirements of the discipline, which are:

Data profiling and data quality measurement: The analysis of data to capture statistics (metadata) that provide insight into the quality of data and help to identify data quality issues.Parsing and standardization: The decomposition of text fields into component parts and the formatting of values into consistent layouts based on industry standards, local standards (for example, postal authority standards for address data), user-defined business rules, and knowledge bases of values and patterns (Bidgoli, 2010).

Generalized “cleansing”: The modification of data values to meet domain restrictions, integrity constraints or other business rules that define when the quality of data is sufficient for an organization.Matching: Identifying, linking or merging related entries within or across sets of data.Monitoring: Deploying controls to ensure that data continues to conform to business rules that define data quality for the organization (Balsmeier, 2006).

Enrichment: Enhancing the value of internally-held data by appending related attributes from external sources (for example, consumer demographic attributes and geographic descriptors).

Lean is an important and proven management technique that business leaders are using to continually improve business performance, service delivery and product development. With its roots in manufacturing, and its focus on value streams and reducing waste, it is now widely deployed in service businesses, including financial services, defense, healthcare providers, retailers and across the public sector. CIOs are successfully using Lean in their IT organizations to improve many aspects of IT’s performance too. Agile approaches to application development are congruent with Lean principles, and those approaches are currently undergoing something of a renaissance, while simultaneously being updated by lessons from Lean Startup.

Understand Your Business Context for Lean

Based on our research and patterns evident in client inquiries, there are four typical reasons CIOs are considering adopting Lean. Only the first three are amenable to Lean:

Because the rest of their enterprise is: In an environment where there is already a high degree of enterprise-level commitment to Lean, often including top executive championship and a history of operational-level success in multiple business units with Lean, CIOs seek to implement Lean in IT to accord with corporate direction.

To improve the value and quality of services provided by IT: In an environment where IT has made use of many of the industry’s process frameworks, guidelines and standards, CIOs are looking for ways to keep the momentum of continual improvement, particularly where the quality management team are seeking increased value creation through faster and tighter integration between IT’s silos.

To enhance IT agility: In an environment demanding rapid business change, making it ever more difficult to keep up with rapidly evolving demand, and to better understand and satisfy customer needs, CIOs seek to implement Lean to rapidly — and at a low cost — figure out what works (via “experiments”), and then be able to scale that up as required, making use of experiential learning and shrinking the time to value for IT projects, creating more agile delivery.

To reduce IT costs: In an environment where businesses continue to endure cost pressure, and IT budgets are flat or falling, CIOs understandably seek to implement Lean as a way to reduce (or avoid) IT costs. Unfortunately, Lean is not well-suited to this task of cost reduction as an end in itself.

Lean Service Evolved as an Adaptation for Service Organizations

While Lean Transformation can work in nonmanufacturing organizations, over the past few years, Lean has speciated into instances that better address the problems faced by different business models. Lean service is one of these evolutions.

Lean service is well-suited to service organizations like IT. At its heart, it works by identifying and eliminating waste to free up capacity to do more value-added work or drive growth and innovation. This waste generally falls into one or more of seven categories:

Overproducing — Making more than is necessary. In IT, this can mean extra copies of output, or unused or unneeded information.

Unnecessary waiting — Too much idle time between process steps. In IT, this can mean scheduled work in the queue waiting to be processed, approvals and batch processing of work.

Unnecessary transportation — Too much work moving between work cells. For IT, this can mean walking a customer request from one department to another department in a call center or excess data movements between storage locations.

Overprocessing — Processing more than is necessary. For IT, this can mean unnecessary cover sheets, fancy backgrounds, unneeded analysis or documentation.

Unnecessary inventory — Too much working capital or stock on hand. For IT, this can be extra supplies, outdated hardware, extra data stores or extra copies of applications.

Unnecessary movement — Time wasted on unnecessary movements of work or tools within a work cell. For IT, this could be “spaghetti” workflow through a department, printers remote from workplace, extra clicks to accomplish tasks and so on.

Too many defects — Things that don’t work as they should. For IT, this can be the supply of wrong devices, wrong print quality, bugs, missed requirements, rework and so forth.

An important insight from Lean service is the recognition of the three different types of work a service organization undertakes. The first, value demand, is the work placed by customers. If a customer orders a service, it is the effort involved in providing it. A second and related form of work is the support work created by the value work itself. This might be management oversight, checking, sign-offs, reviews and so on. It’s essential, but not something the customer pays for willingly.

The third type of work is much more interesting from a Lean perspective. It’s failure demand — the work created to correct something that’s gone wrong.

Although superficially similar to rework in manufacturing, in many service businesses, a service failure has a multiplicative effect, where the cost of rectification far exceeds the cost of doing it correctly in the first place. Common forms of failure demand include errors in the service transactions that have to be corrected; delays that have to be expedited, which wreak havoc with schedules; and poorly communicated changes that deluge the help desk with calls from bewildered and angry customers. Systematically reducing failure demand is a good place to start your Lean efforts.

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