Proposing a Business Model in Healthcare Industry: E-Diagnosis

Proposing a Business Model in Healthcare Industry: E-Diagnosis

Nastaran Hajiheydari (Faculty of Management, University of Tehran, Tehran, Iran), Seyed Behnam Khakbaz (Faculty of Management, University of Tehran, Tehran, Iran) and Hamidreza Farhadi (Faculty of Management, University of Tehran, Tehran, Iran)
DOI: 10.4018/jhisi.2013040104


In modern-day developing countries, there are certain key problems in the healthcare system that adds to a patient’s confusion. An example of these difficulties relates to choosing an appropriate medical specialty and among specialists. Owing to the lack of structural healthcare services, there is the need for guidance in selecting the most appropriate diagnosis and medicine for patients with various symptoms or physical disabilities, the need to educate patients on self-treatment procedures, the need to reduce the high cost of treatment and diagnosis, the need to address boring procedures of diagnosis and treatment, the lack of adequate strategic planning due to the absence of valuable information about patients, the problems connected with unnecessary traffic congestion, and many more. Together, these problems create a great opportunity for the expert analysts to ameliorate the healthcare system in these countries by applying new methods, such as using web-based programs and data mining (DM). This article focuses on the use of software, healthcare data warehouse and the application of DM to generate models for solving the aforementioned problems.
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Literature Review

Owing to widespread use of traditional methods in the healthcare systems, a lot of inefficiencies and challenges have already existed. Examples of problems include the huge amount of paperwork involved in traditional care delivery record-keeping, the unwillingness of health experts who handle patient's files like business records to share data, as well as disintegrated health data (Gates, 1999).

Fortunately, the electronic health record (EHR) can assist in many of these aforementioned problems although there are some larger problems, which cannot be solved by EHR, for example, the confusion often expressed by patients in selecting appropriate medical specialty and among specialists, the frustration with the lack of structural healthcare services, the uncertainty surrounding what would be the most appropriate diagnosis and medicine for patients with various symptoms or physical disabilities, and so on.

Essentially, the EHR system is a store of electronically sustained information about an individual’s lifetime health status and health care; in fact, the EHR is a core part of the patient information systems. As well, the EHR is often considered an instrument which can be used to improve the therapy of patients by dynamic processes (Schramm & Weber, 2001).

Research about online symptom processor showed that these computer assisted systems can greatly improve diagnostic effectiveness. These systems and clinician bring different but complementary types of knowledge to the diagnostic task (Fox, Barber, & Bardhan, 1979). Computers affect diagnosis by assisting a physician to analyze the patient's data both efficiently and effectively (Bruce & Yarnall, 1966).

The previous suggested web-based models are able to determine the correct diagnosis in some of the cases. An advantage of those models is the usage of the Internet as a database with monitoring and updating capabilities. Furthermore, the mentioned models could assist in identifying some of the key issues in a patient’s medical records. As well, these data and models are able to provide insights on a possible diagnosis. Therefore, these e-tools are useful to assist the physician in his review of a patient’s medical records (Segev, Leshno, & Zviran, 2007).

One of the best computer assistance methods that can be helpful for healthcare decision-making is data mining (DM). Briefly, DM is the process of selecting, exploring and modeling large amounts of data in order to discover unknown patterns or relationships. These gained patterns are applied to provide a clear and useful result to the data analyst (Giudici, 2003).

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