Supporting the Billing Process in Outpatient Medical Care: Automated Medical Coding Through Machine Learning

Reimbursement in medical care implies significant administrative effort for medical staff. To bill the treatments or services provided, diagnosis and treatment codes must be assigned to patient records using standardized healthcare classification systems, which is a time-consuming and error-prone task. In contrast to ICD diagnosis codes used in most countries for inpatient care reimbursement, outpatient medical care often involves different reimbursement schemes. Following the Action Design Research methodology, we developed an NLP-based machine learning artifact in close collaboration with a general practitioner’s office in Germany, leveraging a dataset of over 5,600 patients with more than 63,000 billing codes. For the code prediction of most problematic treatments as well as a complete code prediction task, we achieved F1-scores of 93.60 % and 78.22 %, respectively. Throughout three iterations, we derived five meta requirements leading to three design principles for an automated coding system to support the reimbursement of outpatient medical care

Towards a Reference Architecture for Female-Sensitive Drug Management

Due to various biological factors, males and females differ in their response to drug treatment. However, there is still a lack of knowledge of the effects resulting from sex-differences in the medical field, especially due to the issue of underrepresentation of females in clinical studies. Considering severe diseases that are related to the cardiovascular system, which are likely to be perilous, counteracting this lack and emphasizing the need for sex-dependent drug treatment is of high importance. Thus, this research-in-progress paper aims at strengthening the female perspective in drug management by proposing design considerations on IS regarding recommender systems in healthcare for reinforcing shared decision-making and person-centered care. The resulting artefact presented will be a reference architecture with a mobile application as the interface to patients and healthcare professionals as well as a data- driven backend to collect and process data on sex specificity in the medical treatment of cardiovascular diseases (CVD)

Venezuelan Equine Encephalitis and Upper Gastrointestinal Bleeding in Child

Venezuelan equine encephalitis (VEE) is reemerging in Peru. VEE virus subtype ID in Peru has not been previously associated with severe disease manifestations. In 2006, VEE virus subtype ID was isolated from a boy with severe febrile disease and gastrointestinal bleeding; the strain contained 2 mutations within the PE2 region

Genetic Characterization of Venezuelan Equine Encephalitis Virus from Bolivia, Ecuador and Peru: Identification of a New Subtype ID Lineage

Venezuelan equine encephalitis virus (VEEV) has been responsible for hundreds of thousands of human and equine cases of severe disease in the Americas. A passive surveillance study was conducted in Peru, Bolivia and Ecuador to determine the arboviral etiology of febrile illness. Patients with suspected viral-associated, acute, undifferentiated febrile illness of <7 days duration were enrolled in the study and blood samples were obtained from each patient and assayed by virus isolation. Demographic and clinical information from each patient was also obtained at the time of voluntary enrollment. In 2005–2007, cases of Venezuelan equine encephalitis (VEE) were diagnosed for the first time in residents of Bolivia; the patients did not report traveling, suggesting endemic circulation of VEEV in Bolivia. In 2001 and 2003, VEE cases were also identified in Ecuador. Since 1993, VEEV has been continuously isolated from patients in Loreto, Peru, and more recently (2005), in Madre de Dios, Peru. We performed phylogenetic analyses with VEEV from Bolivia, Ecuador and Peru and compared their relationships to strains from other parts of South America. We found that VEEV subtype ID Panama/Peru genotype is the predominant one circulating in Peru. We also demonstrated that VEEV subtype ID strains circulating in Ecuador belong to the Colombia/Venezuela genotype and VEEV from Madre de Dios, Peru and Cochabamba, Bolivia belong to a new ID genotype. In summary, we identified a new major lineage of enzootic VEEV subtype ID, information that could aid in the understanding of the emergence and evolution of VEEV in South America

Arboviral Etiologies of Acute Febrile Illnesses in Western South America, 2000–2007

Over recent decades, the variety and quantity of diseases caused by viruses transmitted to humans by mosquitoes and other arthropods (also known as arboviruses) have increased around the world. One difficulty in studying these diseases is the fact that the symptoms are often non-descript, with patients reporting such symptoms as low-grade fever and headache. Our goal in this study was to use laboratory tests to determine the causes of such non-descript illnesses in sites in four countries in South America, focusing on arboviruses. We established a surveillance network in 13 locations in Ecuador, Peru, Bolivia, and Paraguay, where patient samples were collected and then sent to a central laboratory for testing. Between May 2000 and December 2007, blood serum samples were collected from more than 20,000 participants with fever, and recent arbovirus infection was detected for nearly one third of them. The most common viruses were dengue viruses (genera Flavivirus). We also detected infection by viruses from other genera, including Alphavirus and Orthobunyavirus. This data is important for understanding how such viruses might emerge as significant human pathogens

Supporting the billing process in outpatient medical care: Automated medical coding through machine learning

Reimbursement in medical care implies significant administrative effort for medical staff. To bill the treatments or services provided, diagnosis and treatment codes must be assigned to patient records using standardized healthcare classification systems, which is a time-consuming and error-prone task. In contrast to ICD diagnosis codes used in most countries for inpatient care reimbursement, outpatient medical care often involves different reimbursement schemes. Following the Action Design Research methodology, we developed an NLP-based machine learning artifact in close collaboration with a general practitioner’s office in Germany, leveraging a dataset of over 5,600 patients with more than 63,000 billing codes. For the code prediction of most problematic treatments as well as a complete code prediction task, we achieved F1-scores of 93.60 % and 78.22 %, respectively. Throughout three iterations, we derived five meta requirements leading to three design principles for an automated coding system to support the reimbursement of outpatient medical care

When Every Minute Matters – Using Predictive Analytics of Intervention Durations to Support Hospital Scheduling

Clinical interventions subordinate to medical pathways are characterized by patientspecific complications and variability of process durations. At the same time, estimating these durations is critical for developing accurate schedules. However, data of clinical information systems are recorded primarily for reporting, liability, and billing purposes; the systems do not fully capture detailed process information. Very little work has been done on predicting these types of durations for scheduling, other than using experts’ estimates or historical averages. We evaluate how predictive analytics based on patientspecific features can help develop estimates of otherwise unknown process durations, taking infusion chemotherapy as an example. We highlight the challenges of using clinical real-life data and discuss how we plan to address these challenges in the futur