Using a Multi-Level Process Comparison for Process Change Analysis in Cancer Pathways

The area of process change over time is a particular concern in healthcare, where patterns of care emerge and evolve in response to individual patient needs. We propose a structured approach to analyse process change over time that is suitable for the complex domain of healthcare. Our approach applies a qualitative process comparison at three levels of abstraction: a holistic perspective (process model), a middle-level perspective (trace), and a fine-grained detail (activity). Our aim was to detect change points, localise and characterise the change, and unravel/understand the process evolution. We illustrate the approach using a case study of cancer pathways in Leeds where we found evidence of change points identified at multiple levels. In this paper, we extend our study by analysing the miners used in process discovery and providing a deeper analysis of the activity of investigation in trace and activity levels. In the experiment, we show that this qualitative approach provides a useful understanding of process change over time. Examining change at three levels provides confirmatory evidence of process change where perspectives agree, while contradictory evidence can lead to focused discussions with domain experts. This approach should be of interest to others dealing with processes that undergo complex change over time

Process mining in oncology using the MIMIC-III dataset

Process mining is a data analytics approach to discover and analyse process models based on the real activities captured in information systems. There is a growing body of literature on process mining in healthcare, including oncology, the study of cancer. In earlier work we found 37 peer-reviewed papers describing process mining research in oncology with a regular complaint being the limited availability and accessibility of datasets with suitable information for process mining. Publicly available datasets are one option and this paper describes the potential to use MIMIC-III, for process mining in oncology. MIMIC-III is a large open access dataset of de-identified patient records. There are 134 publications listed as using the MIMIC dataset, but none of them have used process mining. The MIMIC-III dataset has 16 event tables which are potentially useful for process mining and this paper demonstrates the opportunities to use MIMIC-III for process mining in oncology. Our research applied the L* lifecycle method to provide a worked example showing how process mining can be used to analyse cancer pathways. The results and data quality limitations are discussed along with opportunities for further work and reflection on the value of MIMIC-III for reproducible process mining research

The assessment of data quality issues for process mining in healthcare using Medical Information Mart for Intensive Care III, a freely available e-health record database

There is a growing body of literature on process mining in healthcare. Process mining of electronic health record systems could give benefit into better understanding of the actual processes happened in the patient treatment, from the event log of the hospital information system. Researchers report issues of data access approval, anonymisation constraints, and data quality. One solution to progress methodology development is to use a high-quality, freely available research dataset such as Medical Information Mart for Intensive Care III, a critical care database which contains the records of 46,520 intensive care unit patients over 12 years. Our article aims to (1) explore data quality issues for healthcare process mining using Medical Information Mart for Intensive Care III, (2) provide a structured assessment of Medical Information Mart for Intensive Care III data quality and challenge for process mining, and (3) provide a worked example of cancer treatment as a case study of process mining using Medical Information Mart for Intensive Care III to illustrate an approach and solution to data quality challenges. The electronic health record software was upgraded partway through the period over which data was collected and we use this event to explore the link between electronic health record system design and resulting process models

Process Mining of Disease Trajectories: A Literature Review

Disease trajectories model patterns of disease over time and can be mined by extracting diagnosis codes from electronic health records (EHR). Process mining provides a mature set of methods and tools that has been used to mine care pathways using event data from EHRs and could be applied to disease trajectories. This paper presents a literature review on process mining related to mining disease trajectories using EHRs. Our review identified 156 papers of potential interest but only four papers which directly applied process mining to disease trajectory modelling. These four papers are presented in detail covering data source, size, selection criteria, selections of the process mining algorithms, trajectory definition strategies, model visualisations, and the methods of evaluation. The literature review lays the foundations for further research leveraging the established benefits of process mining for the emerging data mining of disease trajectories

Process Mining to Explore Variations in Endometrial Cancer Pathways from GP Referral to First Treatment

The main challenge in the pathway analysis of cancer treatments is the complexity of the process. Process mining is one of the approaches that can be used to visualize and analyze these complex pathways. In this study, our purpose was to use process mining to explore variations in the treatment pathways of endometrial cancer. We extracted patient data from a hospital information system, created the process model, and analyzed the variations of the 62-day pathway from a General Practitioner referral to the first treatment in the hospital. We also analyzed the variations based on three different criteria: the type of the first treatment, the age at diagnosis, and the year of diagnosis. This approach should be of interest to others dealing with complex medical and healthcare processes

Incidence of HIV-infected infants born to HIV- infected mothers with prophylactic therapy: Preliminary report of hospital birth cohort study

Background Human immunodeficiency virus (HIV) is expanding rapidly and was reported double in several places in Indonesia To our knowledge, reports regarding HIV-infected infants are still scarce. Objectives To investigate the incidence of HIV-infected infants born to HIV- mothers who had received prophylaxis therapy at birth. Methods A prospective hospital-based cohort study was held from January 2003 until December 2004 in Cipto Mangunkusumo Hos- pital, Jakarta. The inclusion criteria were mothers with positive HIV and their infants had been given anti retroviral (ARV) therapy. The babies were followed up monthly and the status of infection was determined by PCR at the age of 4 weeks and 6 months. Outcome was measured based on PCR assays or clinical signs of HIV in- fection. Results The mothers’ age ranged from 19 to 27 years. All of them were carrying their first child and only 41% mothers took ARV pro- phylaxis. Almost all mothers underwent caesarean section and the infants had formula feeding. HIV infection was diagnosed in 7 in- fants and 2 of them had RNA assays more than 5,000 copies/ml. Six infants were negatives whereas 3 infants were diagnosed as indeterminate HIV infection and needed further examination. One needed no further investigation as the mother was seronegative. Conclusions Preventing HIV transmission from mother to infant can be done by giving ARV during prenatal, intrapartum, and post- natal period to the newborn. In our hospital, transmission was con- firmed in 6 of 17 infants. Unison protocol must be used and popu- lation of HIV-pregnant mother must be registered in order to know how high the transmission rate among Indonesian HIV peopl

Process Mining in Oncology: a Literature Review

Process mining, an emerging data analytics method, has been used effectively in various healthcare contexts including oncology, the study of cancer. Cancer is a complex disease with many complicated care requirements and there is an urgent need to improve the cost and clinical effectiveness of cancer care pathways. Process mining of the e-health records of cancer patients may play an important future role and this paper presents a literature review of process mining in oncology as a contribution to this research. The search produced 758 articles which were manually reviewed by title, abstract, and full paper text review to develop the original pool of papers. An in-depth ancestor search was used to gather additional articles from the references of the original pool. These steps resulted in 37 papers. Through a thematic review process, the papers were analysed and five themes emerged. These were: 1) process and data types; 2) research questions; 3) techniques, perspectives and tools; 4) methodologies; 5) limitations and future work. This review can: (i) highlight the potential value of process mining for improving cancer care processes (ii) provide a useful overview of the current work undertaken; (iii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches; and (iv) identify research opportunities in this new field of study

Terapi Antiretroviral Lini Kedua pada HIV Anak di RS. Cipto Mangunkusumo

Latar belakang. Akses terhadap terapi antiretroviral (ARV) semakin mudah saat ini dan membuat angka harapan hidup anak terinfeksi HIV semakin panjang. Dalam penanganan jangka panjang anak terinfeksi HIV, salah satu masalah baru yang timbul adalah gagal terapi dan resistensi obat. Tujuan. Menilai karakteristik pasien anak terinfeksi di RS. Cipto Mangunkusumo yang menggunakan terapi ARV lini kedua dan indikasi penggantian ke terapi ARV lini kedua. Metode. Penelitian kohort pasien anak terinfeksi HIV di RS Cipto Mangunkusumo sejak tahun 2002. Kriteria inklusi adalah pasien anak terinfeksi HIV yang berobat di RS Cipto Mangunkusumo sejak tahun 2002 sampai April 2012 dan menggunakan salah satu obat antiretroviral lini kedua. Data yang diambil adalah data demografis, kada CD4, jumlah virus, stadium klinis, dan kombinasi terapi ARV. Hasil. Empatratus empat pasien anak terinfeksi HIV dan 44 (10,9%) menggunakan terapi antiretroviral lini kedua. Sebagian besar (59,1%) gagal terapi adalah kombinasi antara kegagalan virologi, imunologis, dan klinis. Median usia saat memulai terapi ARV lini kedua 69 (26-177) bulan. Median lama subyek menggunakan terapi ARV lini pertama 9 (13-176) bulan. Seluruh subyek penelitian menggunakan lopinavir/ ritonavir sebagai salah satu obat ARV lini kedua dengan kombinasi terbanyak adalah didanosin, lamivudin, dan lopinavir/ritonavir (40,9%). Efek samping didapatkan pada 2 pasien akibat abacavir. Sebagian besar subyek (19/25) yang diperiksa jumlah virus pada 6-12 sesudah menggunakan ARV lini kedua mempunyai hasil tidak terdeteksi. Kesimpulan. Jumlah pasien yang menggunakan terapi ARV lini kedua tidak terlalu banyak karena deteksi kegagalan terapi masih lebih banyak berdasarkan kegagalan klinis dan imunologis

Buku ajar alergi-imunologi anak

Buku ini diterbitkan dengan tujuan untuk menambah perbendaharaan buku ajar di bidang ilmu kesehatan anak pada umumnya dan bidang alergi imunologi anak pada khususnya.xiv, 485 hlm.: ilus.; 25 c