Enhancing Service Quality in Hospitals: Mining Multiple Data Sources

Enhancing Service Quality in Hospitals: Mining Multiple Data Sources

Anirban Chakraborty, Sonal G. Rawat, Susheel Chhabra
DOI: 10.4018/978-1-60960-497-4.ch018
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Large organizations use multiple data sources, centralize processing in these organizations require analysis of huge database originating from various locations. Data mining association rules help perform exploration and analysis of large amounts of data to discover meaningful patterns which can facilitate effective decision-making. The objective of this article is to enhance service quality in a hospital using data mining. The improvement in service quality will help to create hygienic environment and enhance technical competence among staff members which will generate value to patients. A weighting model is proposed to identify valid rules among large number of forwarded rules from various data sources. This model is applied to rank the rules based on patient perceived service parameters in a hospital. Results show that this weighting model is efficient. The proposed model can be used effectively for determining the patient’s perspective on hospital services like technical competence, reliability and hygiene conditions under a distributed environment.
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Review Of Literature

Data mining helps analysis of information (rules) that can mine useful patterns from large databases for decision makers. The discovered knowledge can be referred to as rules describing properties of the data, frequently occurring patterns, clustering of objects in the database which can be used to support various intelligent activities such as decision making, planning and problem solving (Jiawei, Kamber, & Kaufmann, 2007).

Let I ={ i1, i2 .i3,….in }be a set of N distinct literals called items, and D be a set of transactions over I. Each transaction contains a set of items i1, i2, i3,….ik € I. A transaction has an associated unique identifier called TID (Transaction Identification Number). An association rule is an implication of the form A→B, where A, B ⊂ I, and A∩B = null set. A is called the antecedent of the rule, and B is called the consequent. A set of items (such as the antecedent or the consequent of a rule) is called an item set. Each item set has an associated statistical measure called support, denoted as supp. For an item set A⊂ I, supp(A) = s, if the fraction of transactions in D containing A equals to s. A rule A→B has a measure of strength called confidence (denoted as Conf) which is defined as the ratio supp(A∪B) / supp(A).

The problem of mining association rules is to generate all rules A→B that have both support and confidence greater than or equal to some user specified threshold, called minimum support (minsupp) and minimum confidence (minconf), respectively (Hand & Mannila, 2004). For regular associations, supp(AuB) ≥ minsupp, conf(A→B) = supp(A∪B) / supp(A) ≥ minconf.

Synthesizing rules is the process of putting all rules together and to produce valid rules from that. To mine transaction databases for large organizations that have multiple data sources, there are two possible ways.

  • i.

    Putting all data together from different sources to amass a centralized database for centralized processing, possibly using parallel and distributed mining techniques.

  • ii.

    Reusing all promising rules discovered from different data sources to form a large set of rules and then searching for valid rules that are useful at the organization level.

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