Building cloud-based healthcare data mining services

The linkage between healthcare service and cloud computing techniques has drawn much attention lately. Up to the present, most works focus on IT system migration and the management of distributed healthcare data rather than taking advantage of information hidden in the data. In this paper, we propose to explore healthcare data via cloud-based healthcare data mining services. Specifically, we propose a cloud-based healthcare data mining framework for healthcare data mining service development. Under such framework, we further develop a cloud-based healthcare data mining service to predict patients future length of stay in hospital

Supporting Governance in Healthcare Through Process Mining: A Case Study

Healthcare organizations are under increasing pressure to improve productivity, gain competitive advantage and reduce costs. In many cases, despite management already gained some kind of qualitative intuition about inefciencies and possible bottlenecks related to the enactment of patients' careows, it does not have the right tools to extract knowledge from available data and make decisions based on a quantitative analysis. To tackle this issue, starting from a real case study conducted in San Carlo di Nancy hospital in Rome (Italy), this article presents the results of a process mining project in the healthcare domain. Process mining techniques are here used to infer meaningful knowledge about the patient careflows from raw event logs consisting of clinical data stored by the hospital information systems. These event logs are analyzed using the ProM framework from three different perspectives: the control flow perspective, the organizational perspective and the performance perspective. The results on the proposed case study show that process mining provided useful insights for the governance of the hospital. In particular, we were able to provide answers to the management of the hospital concerning the value of last investments, and the temporal distribution of abandonments from emergency room and exams without reservation

Intensifying Patient Flow Management by Using Data Mining Methods

Health care organizations need means to process the patient flow-related data into meaningful information in order to understand and manage complex patient flow networks. In this paper we present a theoretical framework of the patient flow management process and examine how this process could be intensified with the help of data mining methodology. Based on the empirical findings in a case organization, a Finnish special care hospital, the potential of suitable data mining methods, like sequence analysis, is evident. Using the data mining methodology, huge amounts of data could be processed into relevant and reliable information to support decision-making. As a result, the overall quality and effectiveness of the patient flow network would be improved

IMPLEMENTASI AKUNTANSI LINGKUNGAN TERHADAP PENGELOLAAN LIMBAH RUMAH SAKIT SILOAM JEMBER

Environmental accounting is an accounting treatment with the inclusion of environmental cost into the accounting practices of companies or government agencies. Hospital are service companies engaged in the health sector which in their operations can have a positive or negative impact on the community in the form of waste. Hospital waste is  all waste generated by a hospital as a result of its operational activities which can contain hazardous and toxic active ingredients that can transmit disease. This study aims to determine how the application and differences of environmental accounting on waste management at Siloam Jember Hospital are related to identification, measurement, recognition, presentation, and disclosure. The type of research use in this research is descriptive qualitative. The data obtained in this study are based on primary data in the form of interview and observations, and based on secondary data in the form of documentation which will then be compared with PSAK No. 33 about General Mining Accounting. The results of this study indicate that the costs incurred as a result of hospital waste management consist of machine procurement costs, maintenance costs, repair costs, depreciation costs, and transportation service costs

Improving hospital layout planning through clinical pathway mining

Clinical pathways (CPs) are standardized, typically evidence-based health care processes. They define the set and sequence of procedures such as diagnostics, surgical and therapy activities applied to patients. This study examines the value of data-driven CP mining for strategic healthcare management. When assigning specialties to locations within hospitals—for new hospital buildings or reconstruction works—the future CPs should be known to effectively minimize distances traveled by patients. The challenge is to dovetail the prediction of uncertain CPs with hospital layout planning. We approach this problem in three stages: In the first stage, we extend a machine learning algorithm based on probabilistic finite state automata (PFSA) to learn significant CPs from data captured in hospital information systems. In that stage, each significant CP is associated with a transition probability. A unique feature of our approach is that we can generalize the data and include those CPs which have not been observed in the data but which are likely to be followed by future patients according to the pathway probabilities obtained from the PFSA. At the same time, rare and non-significant CPs are filtered out. In the second stage, we present a mathematical model that allows us to perform hospital layout planning decisions based on the CPs, their probabilities and expert knowledge. In the third stage, we evaluate our approach based on different performance measures. Our case study results based on real-world hospital data reveal that using our CP mining approach, distances traveled by patients can be reduced substantially as compared to using a baseline method. In a second case study, when using our approach for reconstructing a hospital and incorporating expert knowledge into the planning, existing layouts can be improved

Generator breast datamart\u2014the novel breast cancer data discovery system for research and monitoring: Preliminary results and future perspectives

Background: Artificial Intelligence (AI) is increasingly used for process management in daily life. In the medical field AI is becoming part of computerized systems to manage information and encourage the generation of evidence. Here we present the development of the application of AI to IT systems present in the hospital, for the creation of a DataMart for the management of clinical and research processes in the field of breast cancer. Materials and methods: A multidisciplinary team of radiation oncologists, epidemiologists, medical oncologists, breast surgeons, data scientists, and data management experts worked together to identify relevant data and sources located inside the hospital system. Combinations of open-source data science packages and industry solutions were used to design the target framework. To validate the DataMart directly on real-life cases, the working team defined tumoral pathology and clinical purposes of proof of concepts (PoCs). Results: Data were classified into \u201cNot organized, not \u2018ontologized\u2019 data\u201d, \u201cOrganized, not \u2018ontologized\u2019 data\u201d, and \u201cOrganized and \u2018ontologized\u2019 data\u201d. Archives of real-world data (RWD) identified were platform based on ontology, hospital data warehouse, PDF documents, and electronic reports. Data extraction was performed by direct connection with structured data or text-mining technology. Two PoCs were performed, by which waiting time interval for radiotherapy and performance index of breast unit were tested and resulted available. Conclusions: GENERATOR Breast DataMart was created for supporting breast cancer pathways of care. An AI-based process automatically extracts data from different sources and uses them for generating trend studies and clinical evidence. Further studies and more proof of concepts are needed to exploit all the potentials of this system

Data Mining Approach to Clinical Decision Support System for Hypertension Management

Background: This study examined the utility of data mining algorithms for the management of hypertension. Methods: We studied 2,446 hospitalized patients with hypertension and 3,835 clinic patients with hypertension. Among data mining algorithms, we used clustering analysis and compared decision tree analysis with logistic regression. Results: On the contrary to the previous studies, decision tree performed better than logistic regression. We have also developed a CDSS (Clinical Decision Support System) with three modules (doctor, nurse, and patient) based on data warehouse architecture. Data warehouse collects and integrates relevant information from various databases from hospital information system. Conclusions: This study suggests that data mining algorithms may be an useful method for hypertension management and CDSS system can help improve decision making capability of doctors and improve accessibility of educational material for patients.ope

Rule-based Disease Classification using Text Mining on Symptoms Extraction from Electronic Medical Records in Indonesian

Recently, electronic medical record (EMR) has become the source of many insights for clinicians and hospital management. EMR stores much important information and new knowledge regarding many aspects for hospital and clinician competitive advantage. It is valuable not only for mining data patterns saved in it regarding the patient symptoms, medication, and treatment, but also it is the box deposit of many new strategies and future trends in the medical world. However, EMR remains a challenge for many clinicians because of its unstructured form. Information extraction helps in finding valuable information in unstructured data. In this paper, information on disease symptoms in the form of text data is the focus of this study. Only the highest prevalence rate of diseases in Indonesia, such as tuberculosis, malignant neoplasm, diabetes mellitus, hypertensive, and renal failure, are analyzed. Pre-processing techniques such as data cleansing and correction play a significant role in obtaining the features. Since the amount of data is imbalanced, SMOTE technique is implemented to overcome this condition. The process of extracting symptoms from EMR data uses a rule-based algorithm. Two algorithms were implemented to classify the disease based on the features, namely SVM and Random Forest. The result showed that the rule-based symptoms extraction works well in extracting valuable information from the unstructured EMR. The classification performance on all algorithms with accuracy in SVM 78% and RF 89%

Secure and Trustable Electronic Medical Records Sharing using Blockchain

Electronic medical records (EMRs) are critical, highly sensitive private information in healthcare, and need to be frequently shared among peers. Blockchain provides a shared, immutable and transparent history of all the transactions to build applications with trust, accountability and transparency. This provides a unique opportunity to develop a secure and trustable EMR data management and sharing system using blockchain. In this paper, we present our perspectives on blockchain based healthcare data management, in particular, for EMR data sharing between healthcare providers and for research studies. We propose a framework on managing and sharing EMR data for cancer patient care. In collaboration with Stony Brook University Hospital, we implemented our framework in a prototype that ensures privacy, security, availability, and fine-grained access control over EMR data. The proposed work can significantly reduce the turnaround time for EMR sharing, improve decision making for medical care, and reduce the overall costComment: AMIA 2017 Annual Symposium Proceeding

Enhanced manufacturing storage management using data mining prediction techniques

Performing an efficient storage management is a key issue for reducing costs in the manufacturing process. And the first step to accomplish this task is to have good estimations of the consumption of every storage component. For making accurate consumption estimations two main approaches are possible: using past utilization values (time series); and/or considering other external factors affecting the spending rates. Time series forecasting is the most common approach due to the fact that not always is clear the causes affecting consumption. Several classical methods have extensively been used, mainly ARIMA models. As an alternative, in this paper it is proposed to use prediction techniques based on the data mining realm. The use of consumption prediction algorithms clearly increases the storage management efficiency. The predictors based on data mining can offer enhanced solutions in many cases.Telefónica, through the “Cátedra de Telefónica Inteligencia en la Red”Paloma Luna Garrid