Agaraoli Aravazhi

• Aravazhi, Agaraoli; Helgheim, Berit & Aadahl, Petter (2023). Decision-Making Based on Predictive Process Monitoring of Patient Treatment Processes: A Case Study of Emergency Patients. Advances in Operations Research. ISSN 1687-9147. p. 1–10. doi: 10.1155/2023/8867057. Show summary

  This paper investigates predictive process monitoring problems in emergency treatment by combining the fields of process management and artificial intelligence. The objective is to predict the next activity and its timestamp in the treatment of emergency patients who have undergone surgery at the gastroenterology or urology surgery units in a hospital in Norway. To achieve this goal, three models were developed using different algorithms, and the best performing model was identified using various performance metrics. The results demonstrate the potential of predictive process monitoring to accurately forecast the outcome of patient treatments. By leveraging the insights gained from predictive process monitoring, hospitals can make more informed decisions. The findings of this study suggest that predictive process monitoring holds significant promise as a tool for improving the efficiency and effectiveness of emergency patient treatment processes. This research has significant implications for the field of decision sciences, particularly regarding resource allocation, reducing waiting times, and improving patient outcomes. The ability to predict the outcomes of patient treatment processes has important implications for hospitals, allowing the streamlining and acceleration of the treatment process. Overall, this study provides a promising framework for predicting patient treatment processes by using the predictive process monitoring method. This could be expanded upon in future research, ultimately leading to improved patient outcomes and better decision-making in healthcare.

• Aravazhi, Agaraoli (2021). Hybrid machine learning models for forecasting surgical case volumes at a hospital. AI. 2(4), p. 512–526. doi: 10.3390/ai2040032. Full text in Research Archive Show summary

  Abstract Recent developments in machine learning and deep learning have led to the use of multiple algorithms to make better predictions. Surgical units in hospitals allocate their resources for day surgeries based on the number of elective patients, which is mostly disrupted by emergency surgeries. Sixteen different models were constructed for this comparative study, including four simple and twelve hybrid models for predicting the demand for endocrinology, gastroenterology, vascular, urology, and pediatric surgical units. The four simple models used were seasonal autoregressive integrated moving average (SARIMA), support vector regression (SVR), multilayer perceptron (MLP), and long short-term memory (LSTM). The twelve hybrid models used were a combination of any two of the above-mentioned simple models, namely, SARIMA–SVR, SVR–SARIMA, SARIMA–MLP, MLP–SARIMA, SARIMA–LSTM, LSTM–SARIMA, SVR–MLP, MLP–SVR, SVR–LSTM, LSTM–SVR, MLP–LSTM, and LSTM–MLP. Data from the period 2012–2018 were used to build and test the models for each surgical unit. The results indicated that, in some cases, the simple LSTM model outperformed the others while, in other cases, there was a need for hybrid models. This shows that surgical units are unique in nature and need separate models for predicting their corresponding surgical volumes. View Full-Text Keywords: time series, seasonal autoregressive integrated moving average, machine learning, hybrid model, demand, hospital, surgical unit

• Aravazhi, Agaraoli; Helgheim, Berit & Jæger, Bjørn (2020). Investigation of the influence of product variety on inventories in hospitals. Engineering Management in Production and Services. ISSN 2543-6597. 12(1), p. 34–44. doi: 10.2478/emj-2020-0003. Full text in Research Archive Show summary

  The literature on product variety provides great insights into various businesses. However, little research has been conducted on product variety in the healthcare industry. This study aims to explore the influence of product variety on inventory in hospitals. Since most hospitals are known to replenish products using a homegrown ad-hoc system, a model is developed for exploring all possible product combinations and substitutions. This article presents the behaviour of product substitution, which may be either one-to-one or many-to-one for both sterile and non-sterile products, in the hospital with cost factors. It discusses the product variety reduction and its corresponding cost impacts. The data on a hospital inventory over the course of six years has been procured from a hospital in Norway. Based on the results, the hospital could have a potential product variety reduction of approximately 11% and cost savings from the spending of approximately NOK 3.6 million. Reducing the variety of products in hospital inventories proves to be an approach to reducing costs. The model developed for the research is universal in nature and could be used in other fields, such as retail, marketing etc.

• Müller, Falko & Aravazhi, Agaraoli (2020). A new generalized travel cost based connectivity metric applied to Scandinavian airports. Transportation Research Part D: Transport and Environment. ISSN 1361-9209. 81(April ), p. 1–21. doi: 10.1016/j.trd.2020.102280. Full text in Research Archive Show summary

  This article proposes a new, generalized travel cost based method to operationalize network accessibility provided by airports. The approach is novel as it integrates features of network topology with multiple quality aspects of scheduled air transport services into one metric. The method estimates generalized travel costs for the full set of feasible travel paths between an airport and all network destinations. Rooftop modeling accounts for schedule delay and isolates the most cost-efficient travel paths per O-D relation. Respecting the assumed arrival time preference of passengers and adjusting for destination importance, connectivity scores are derived. The method is then applied to explore changes in the global connectivity pattern of Scandinavian airports from 2004 to 2018. The results suggest distinct spatial differences throughout the network, but less pronounced in size than suggested by popularly applied connectivity measures. Findings also highlight the importance of the geographical location as a determinate of an airport’s connectivity. Keywords: Air transport; Accessibility; Connectivity; Network topology; Air service features.

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• Aravazhi, Agaraoli; Helgheim, Berit & Sandbæk, Birgithe Eckermann (2019). Identifying KPI’s by using text mining of administrative board documents in Norwegian hospitals. Show summary

  This paper analyses 36,000 documents by using text mining from board meetings in hospitals in Norway in the period 2008-2017. The purpose is to explore which subjects are emphasized and identify changes in strategic focus during the time span.

• Helgheim, Berit; Aravazhi, Agaraoli & Løfaldli, Bjarte Bye (2018). Using AI to develop a triage system in Norwegian Home Care. Show summary

  In Norway,the digital alarm system is connected to a response center. In these response centers,nurses are deciding the medical intervention based on patients information. Inthis paper we present how artificial intelligence can be used to automatize andimprove the triage system.

• Aravazhi, Agaraoli & Helgheim, Berit (2018). Text mining of administrative board hospital documents. Show summary

  In Norway, all the hospitals are public owned and divided into four regional healthcare authorities (RHAs). We have analyzed 36,000 documents in the period 2008 – 2017.The purpose is to explore which subjects are emphasized and identify changes in strategic focus during the time span. 

• Jæger, Bjørn & Aravazhi, Agaraoli (2018). Preparing the digital workforce : best-practices in teaching SAP.
• Aravazhi, Agaraoli (2024). From data to decisions : a path towards AI-driven hospital production processes. Molde University College - Specialized University in Logistics. ISSN 978-82-7962-357-1.
• Bach, Terje; Aravazhi, Agaraoli & Konovalenko, Anna (2020). Blockchain technology for electronic health records : challenges & opportunities. Molde University College - Specialized University in Logistics. ISSN 978-82-7962-300-7.
• Aravazhi, Agaraoli & Jæger, Bjørn (2017). Predictive Offshore Crane Maintenance Manager (PCM). Molde University College - Specialized University in Logistics. ISSN 978-82-7962-228-4. Show summary

  This paper reports on the project “From time-based to condition-based maintenance – offshore cranes.” (In Norwegian: Fra tids- til belastningsstyrt vedlikehold – offshore kraner) conducted by Molde Engineering AS from January 13th until May 8th 2017. The project was supported by VRI, the Norwegian Research Council's support mechanism for research and innovation in Norway's regions. We describe the background for the project, the goals, and the results. Keywords: offshore crane, predictive maintenance, relational database, user interface tool

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