Quality Problems in information systems

8 Feb

For this blog I will discuss the problems of Data quality management in the healthcare system and the areas that are being identified as being the problem areas in relation to this topic. I will be discussing the 3 main problems that were identified as being key contributors to quality problems. I will go through each one individually to see what particular issue does arise.  Also I will put into context the impact it has.

Failure to Understand Consumers’ Needs

“A need exists to recognize the information requirements of two different types of information consumer – both the external and the internal customer. Doctors need timely data to assess patient condition and evidence-based information on best practice for treatment etc. The forgotten consumer in healthcare is hospital management, who need to be able to plan, resource and finance the entire process in an efficient manner. Treatment cost information is frequently lost in the system so value for money cannot be determined; beds lie empty while patients remain on waiting lists.” Ó Riain & Helfert (2005)

This highlights how essential it is to have the correct information for each person working in a business. Without this it does effect how a business can be run if its employees cannot work to their full potential.


Poorly Defined Information Production Process

“Information tends to be produced by organizations within national health industries in non-standard ways. The importance of standards and clearly defined industry-wide information production methods are essential in healthcare. Data records for a patient are of little use in one hospital if the patient is being treated in a different setting, where the records cannot be accessed and have no meaning” Ó Riain & Helfert (2005)

This shows the problem if data is not readily available in one place and is in another. Data should be made available across all the systems in order to achieve quality in information systems for a business.


No Product Life Cycle

“In the absence of the EHR in most healthcare settings, paper based processes have evolved to satisfy requirements. One of these processes dictates that at one studied hospital, healthcare episode files are shipped off site 2 months after the episode occurs due to on-site storage constraints (Ó Riain and Helfert, 2004). Effectively, once the immediate information requirement has been satisfied in the system and the patient is discharged, the tendency is to mark the issue as ‘solved’ and neglect to maintain and improve it. However, this information continues to be useful after it is rendered more difficult to access, especially if the patient presents again with similar symptoms after the files have been moved” Ó Riain & Helfert (2005)

Data needs to be stored in order to access it at any time as it may become relevant in the future. Another example of this would be a university. They need to store results for years as they may need to be accessed years after the results are initially issued.

The impact of poor data quality

“Data quality is a notoriously difficult concept to measure. Redman (1996) estimates that the typical industrial data quality error rate of 1-5% can constitute a 10% loss in revenue. The Institute of Medicine (2000) believes that poor data quality is responsible for up to 98,000 deaths in U.S. hospitals each year. Eckerson (2002) estimates that data quality problems cost U.S. businesses more than $600 billion per year. Health providers and insurers are not immune to this”  Ó Riain & Helfert (2005)

Overall the impact does seem to be quite significant. The amount lost each year is very substantial. It does provide dividends for a company to accurately store data so it can be accessed efficiently. It reaps rewards as the business can maximise their potential with it.


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