An Artificial Neural Network Classification of Prescription Nonadherence

An Artificial Neural Network Classification of Prescription Nonadherence

Steven Walczak (School of Information & Florida Center for Cybersecurity, University of South Florida, Tampa, FL, USA) and Senanu R. Okuboyejo (Covenant University, Ota, Nigeria)
DOI: 10.4018/IJHISI.2017010101
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Abstract

This study investigates the use of artificial neural networks (ANNs) to classify reasons for medication nonadherence. A survey method is used to collect individual reasons for nonadherence to treatment plans. Seven reasons for nonadherence are identified from the survey. ANNs using backpropagation learning are trained and validated to produce a nonadherence classification model. Most patients identified multiple reasons for nonadherence. The ANN models were able to accurately predict almost 63 percent of the reasons identified for each patient. After removal of two highly common nonadherence reasons, new ANN models are able to identify 73 percent of the remaining nonadherence reasons. ANN models of nonadherence are validated as a reliable medical informatics tool for assisting healthcare providers in identifying the most likely reasons for treatment nonadherence. Physicians may use the identified nonadherence reasons to help overcome the causes of nonadherence for each patient.
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Introduction

Prescription (pharmaceutical) treatment plans require a specific pharmaceutical drug to be taken in specific amounts at specific intervals for a specific period of time. Nonadherence is the violation of any of these specified treatment requirements: not taking the correct dosage (too little or too much), missing or delaying scheduled administrations of the pharmaceutical, or not completing the treatment (Hugtenburg et al., 2013). Nonadherence to pharmaceutical treatment plans is a worldwide dilemma and prior research results examining the rate of nonadherence in 20 countries are shown in Table 1. As may be seen, documented nonadherence rates vary in different cultures, but range from 10% to 88%. Prescription nonadherence is a persistent problem in healthcare today (Lehane & McCarthy, 2007) and nonadherence through underuse of a medication is rising significantly in prevalence (Kirking et al., 2006).

Table 1.
Documented nonadherence rates from around the world
CountryNonadherence
rates
Reference
Australia/New Zealand16.4%(Hirth et al., 2012)
Belgium12%
18%
(Hirth et al., 2012)
(Pechère, 2001)
Canada15%(Hirth et al., 2012)
China57-64%(Zhu et al., 2016)
Columbia41%(Pechère, 2001)
France12.3%
18%
(Hirth et al., 2012)
(Pechère, 2001)
Germany12.9%(Hirth et al., 2012)
India17%(Agarwal, Yewale, & Dharmapalan, 2015)
Indonesia43%(Andrajati et al., 2016)
Italy13.1%
35%
(Hirth et al., 2012)
(Pechère, 2001)
Japan10.9%(Hirth et al., 2012)
Malaysia29%(Agarwal, Yewale, & Dharmapalan, 2015)
Morocco38%(Pechère, 2001)
Spain12.7%
45%
(Hirth et al., 2012)
(Pechère, 2001)
Sweden11.8%(Hirth et al., 2012)
Thailand47%(Pechère, 2001)
Turkey30%(Pechère, 2001)
United Kingdom10%
13.2%
(Pechère, 2001)
(Hirth et al., 2012)
United States7% - 45%
18%
20 – 80%
38%
45% – 88%
50%
56.2%
(Kirking et al., 2006)
(Hirth et al., 2012)
(Gottlieb, 2000)
(Au et al., 2014)
(Gamble, 2009)
(Buckalew & Sallis, 1986;
Wapner, 2008)
(Gibson et al., 2011)
Worldwide0% - 95.4%(DiMatteo, 2004)

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