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Data Mining Benefits, Limitations, and Challenges

Data are one of the most important tools in any business organizations. Whatever type of business you are running, you need valuable data to make short-term or long term plan, market your business, and increase your sales. For example, data that can be gleaned from customer survey will help managers to align his or her marketing plan and strategies to target a certain segment of the market to increase sales. Data that can be gathered from employee's performance, on the other hand, will help managers to decide either to promote an employee or layoff.

In Pharma sector, data are valuable sources of information. Primary source of data in Pharma sector comes from administrative, financial, organizational rules of general guidelines, data from clinical trials for various drug development projects and other clinical data collected from patient, medical treatment, and length of stay of patient in the hospital. Though valuable and abundant, these data are raw and unusable. Data mining uses sophisticated data analysis tools to manage these data and convert it into an understandable forms and formats to help managers, decision-makers, and health care providers develop and test hypotheses, derived conclusions, and identify trends and opportunities (Ranjan, 2009, p.82/84).

What is Data Mining?

Data mining is defined as the process of extracting information from large data sets through the use of algorithms and techniques drawn from the field of statistics, machine learning and data base management systems (Fedlers et al., 2000).

Apart from collecting and managing data, data mining uses tools like association, clustering, segmentation, and classification to provide objective analysis and prediction.

Compared to the "traditional methods of data collection that involve manual work and interpretation", data mining utilizes state-of-the-art technology to gather and analyze data from various sources. Traditional method is "slow, expensive and highly subjective". Data mining is fast, economical, and highly objective.

Data Mining Uses and Benefits

Data mining offers a number of uses and benefits. Both private and public sectors such as banking, insurance, pharmaceutical manufacturers, health care providers, and retailing are using data mining for a variety of purpose to reduce costs, enhance research, predict the effectiveness of a procedure or medicine, and increase sales.

Ranjan (2009) identified the following benefits and uses of data mining in hospital and Pharma sector:

  • Data mining is used to support clinicians at the point of care delivery, controlling clinical treatment pathways, administrative and management tasks, and efficient management of organizational and financial (p.84).
  • The main techniques of data mining used in Pharma sector are clustering, classification, and association. Clustering is used to group drugs most likely or unlikely to be used, group top drugs, etc. Classification is used to predict consumer behavior, predict the likelihood of success in a drug adoption process, ect. Association is used to association of training undertaken diseases with drugs, association and analysis of staff movements, etc (p.85).
  • Most health care providers don't have enough time to fill out reports of possible adverse drug reactions. And for pharmaceutical companies, it is expensive and time consuming to perform a thorough data collection, especially when most of the information is not required by law. Through data mining techniques, an easy to use decision support platform that support platform that supports decision-making process of care givers and clinical managers can be built (p.86).
  • Medical institutions as well as health insurance companies have primarily interest in increasing the patient healing rate and reducing treatment costs. Application of data mining could prove economical in the long term by avoiding duplication of data examinations, time saved through automation of routine tasks and simplification of accounting and administrative procedures (p.86).
  • Data mining brings alerts at the point of patient care and supports clinical or business strategies and provides standing or ad hoc reports for care givers (clinicians, physicians, nurses) and other decision-makers such as clinical management and human resources (p.86).
  • Data mining can provide intelligence in terms of drug positioning information, patent population characteristics, indications of what the drug is being used for, prescribing physician characteristics, regional preferences, prevalence of diseases, etc (p.87).
  • In research and development of a new drug, data mining can help find drugs that have desirable activity; predict who will benefit from drug; regulate drugs testing; identify trends, irregularity, and risk during product development and launch; and can help examine consumer behavior in terms of prescription renewal and product purchases, etc. (p.88-91).
Limitations

Although data mining application is a very powerful tool, it cannot stand alone by itself. To be successful, data mining needs a skilled user who will supply the correct data and a specialist who can make objective conclusions out of the output that is created. If the user supplies incorrect or minimal amount of information, output will be affected and forecast will not be credible.

Furthermore, while data mining helps the user discover patterns and relationships in data, it cannot promise perfect results, cannot explain why an outcome occurs, and cannot correct problems in your data.

Future and Challenges

The challenge is to be able to harness and utilize the power of technology to design data mining application that is reliable, easy to use, and supports decision-making process. As the amount of data increases in the future, data mining will become a valuable tool both in hospital and Pharma sector. In hospital, for example, data mining application will help protect patient by maintaining accurate records of patient's treatment, lab test, and administration of prescriptions. In Pharmaceutical industry, data mining will be helpful in finding new quality drugs, predict who will be benefit from drug, and can help optimize use of sales resources like manpower and marketing (Jayanthi, 2009, p. 90-91).

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References:
  • Ranjan,J(2009): Data Mining in Pharma Sector : Benefit. Ghaziabad, India: Emerald. Vol 11, p. 82-93.

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Why Pharmacoeconomics is Important?

Drug therapy plays a crucial role in treating patients. According to Bootman, Townsend, and Mcghan(2011),hospitals spend billions of dollars every year on drugs and drug products to treat hospitalized patients. In United States, sales of non-prescription drugs bought over-the-counter have increased from $700 million in 1950s to billions of dollars today (p.1-2).

Pharmaceutical companies are constantly developing and releasing new drugs. Every six months, there are new drugs in the market and there are several more in the research, laboratories, and development network. Due to the multiplicity of drugs on the market and the introduction of the new one, decision makers are "faced with difficult choices" about what type of drugs...read more.

 

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