In this study, an electronic referral system was developed for general practitioners to send referrals electronically to providers. The electronic referral system aims at improving referral decisions by involving patients in the process. A database of hospital services in Lagos metropolis was developed and hospitals distance information were retrieved and computed using Google map. A provider selection model that uses a multi-attribute decision making function was adapted and implemented. The provider selection model selects optimal provider based on patients and providers determinants which contained fourteen criteria for referral decision. In the system's output, hospitals were ranked by computing the average between provider and patient feedback factors, this differs from existing systems as the implemented system shows how introduction of patient participation can affect recommended hospitals. In conclusion, the result of this work is expected to improve referral decision support and patient participation.

Iris recognition algorithms have been proposed in several works with some of these algorithms solving mainly templates identification accuracy issues. The need to test these algorithms for identification or matching speed cannot be over-emphasized as this is also important when deploying algorithms in real application. This aim of this paper is to implement and validate a selected iris recognition algorithm. Performance evaluation was performed with the sole purpose of verifying the literature reported accuracy for the selected algorithm as well as to compute its identification speed on two databases (CASIA and BuIris) containing 600 iris images each. Results obtained matched the earlier 0% false acceptance with CASIA database but 42.3% with BuIris. This paper results verifies the scope of this algorithm and the need for improvement that could increase its adoptability globally.

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