Question 1 (100 marks) Study the article on \"Intelligent decision support in he

Question

Question 1 (100 marks) Study the article on "Intelligent decision support in healthcare" (Basu et al., 2012). Then, answer all the following questions in your own words. (a) Differentiate between a Decision Support System (DSS) and an Intelligent DSS (IDSS) 10 marks (b) Discuss the common uses of IDSS in a healthcare environment. [10 marks] (c) Relate the IDSS model described in the article to the components shown in Figure 1. Explain each component with suitable examples. User Interface Knowledge Management Data Management Model Management External and Internal Data Figure 1: Basic components of a decision support system - adopted from course material Unit 1, pp.22. 40 marks]

Explanation / Answer

1. Difference betweed DSS and IDSS:

An IDSS leads to development of knowledge in specific domain by identitfying and gathering the strategically useful information patterns from raw data i.e. making it readable and understandable to a wider audience, which helps in decision-making. IDSS, unlike DSS, “allows for supporting a wider range of decisions including those with uncertainty”. IDSS makes extensive use of AI (Artificial Intelligence) where as Decision support system does it more at a manual level by using various algorithms.IDSS, in addition to giving recommendations, may also contribute estimates of the level of confidence in the recommendations it gives.

IDSS can handle complex problems, applying domain-specific expertise to assess the consequences of executing its recommendations. Decisions supported by IDSS also tend to be more consistent, timely and better managed in terms of managing uncertainty in the outcomes. The justification of outcomes provided by an IDSS is particularly significant if it allows clinical experts to validate the explanations provided by the IDSS.

2. Common uses of IDSS in HealthCare Department:

Clinical decision support systems (CDSS) can play a significant role in healthcare. The clinical decisions taken by healthcare service providers are mostly based on clinical guidance and evidence-based rules derived from medical science. When it comes to intelligent decision support systems (IDSS), the interpretive analysis of large-scale patient data with intelligensed methods allow doctors and nurses to quickly gather information and knowledge-bamation and process it in various ways in order to assist with making diagnosis and treatment decision. IDSS can be applied in healthcare in diverse areas such as the examination of real-time data from diverse monitoring devices, analyses of patient and family history for the purpose of diagnosis, reviews of common characteristics and trends in medical record databases and many more areas.

A hybrid architecture combining the concepts of data mining (DM) and artificial neural networks (ANN) can be applied to patient data for intelligent decision support in healthcare. An IDSS in healthcare gathers and incorporates healthcare-specific domain knowledge and performs intelligent actions, including learning and reasoning while recommending clinical steps to take and justifying the outcomes.

3. Various components in IDSS:

A customer comes to the bank to request a mortgage loan to buy a house. The bank employee working with the customer will collect information to decide whether the bank will provide a loan to the customer and under what conditions. The information collected includes things like the customer's employment, income, credit score, loan history and other financial information.

Since this loan is to buy a house, the bank also collects information on the property, such as the legal description and the assessed market value. The bank will also look at trends in the real estate market, including interest rates offered by other financial institutions. Finally, the bank needs to consider its own internal finances, such as the funds it has available for loans, how many mortgage loans it has already approved recently, its experience with loans given to similar customers, etc.

There is a lot of information to consider. Some of this can be used again for the next loan application, but some of it is very specific to this particular customer. Some of the information can also change very quickly, such as trends in the housing market. A DSS makes it possible for the bank employee to make an informed decision in a timely manner that considers all the different internal and external data sources.

4. Which approach is better for developing a data warehouse in healthcare environment:

Inmon Apporach:

Kimball Approach:

There is a benefit to the independent data model approach: it takes less time for the organization to build the data warehouse, and analysts can start to analyze data quickly — a big difference from the two- to five-year lifecycle of the enterprise model. However, it grows very quickly, as do the data streams, until several redundant streams exist. This creates a challenge for those trying to maintain the model. If one underlying source system changes, they have to change each extraction routine that uses that particular source.

The many isolated data marts also means there is not an atomic-level data warehouse from which to build additional data marts in the future. Some other failures:

This approach may present information about a certain metric falling below the benchmark, but it does not contain the granular data that enables the analyst to dig down and determine why that metric is low. Without that more detailed information, it is difficult to make the data actionable and to determine how to bring that metric up to the benchmark.

This approach causes source systems to be repetitiously and unnecessarily bombarded by data extracts, which slows down the system. Redundant feeds from each source system need to be built to feed each independent data mart. This creates a challenge for those trying to maintain the model. If one underlying source system changes, then each extraction routine that uses that particular source needs to be changed.

Like inmon approach it also reuqires early binding of data and thus has the common failures.

To conclude a third approach should be adopted for developing health care systems which is the late binding system. Binding data later means delaying the application of business rules, such as data cleansing, normalization, and aggregation for as long as possible. This provides health systems with time to review and revise data, form hypotheses, and determine optimal analytic uses. In addition, there is no longer a need to make lasting decisions about the data model upfront, which is useful, since it is difficult to see what new information will come down the road in two, three, or five years. By binding late, analysts only need to bind the data when there is an actual clinical or business problem to solve.

But if we are supposed to choose one from the above two approaches, the kimball approach is better, given that its benifits are far more fitting than then inmon approach and has less consequences when compared to the inmon approach.

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