Authorization and access control of application data in Workflow systems

Workflow Management Systems (WfMSs) are used to support the modeling and coordinated execution of business processes within an organization or across organizational boundaries. Although some research efforts have addressed requirements for authorization and access control for workflow systems, little attention has been paid to the requirements as they apply to application data accessed or managed by WfMSs. In this paper, we discuss key access control requirements for application data in workflow applications using examples from the healthcare domain, introduce a classification of application data used in workflow systems by analyzing their sources, and then propose a comprehensive data authorization and access control mechanism for WfMSs. This involves four aspects: role, task, process instance-based user group, and data content. For implementation, a predicate-based access control method is used. We believe that the proposed model is applicable to workflow applications and WfMSs with diverse access control requirements

Needs Assessment for a Patient Centered Medical Home Model of Care at the Providence Alaska Cancer Center

Presented to the Faculty of the University of Alaska Anchorage in Partial Fulfillment of the Requirements for the Degree of MASTER OF PUBLIC HEALTHIn order to better understand the needs of cancer patients and allocate resources, the Providence Alaska Cancer Center requested a needs assessment for an oncology focused patient centered medical home (PCMH). A PCMH allows for coordinated and comprehensive care through the use of a teamwork model that centers on the primary care physician. The Providence Alaska Cancer Center staff randomly selected the records of 200 cancer patients between 2010 and 2011, using the cancer tumor registry. Data were analyzed to answer four specific questions that addressed the 1) presence of a Primary Care Physician (PCP), 2) number and type of comorbidities, 3) cancer diagnosis and 4) insurance status impacted emergency room utilization. Individuals tended to utilize the emergency room more if they 1) had a PCP, 2a) had three or more comorbidities, 2b) were diagnosed with hyperlipidemia, chronic obstructive pulmonary disease (COPD) or hypertension, 3) were diagnosed with an “other” cancer as opposed to breast, lung or gynecological cancers or 4) had federal insurance. These data in particular show expected trends such as patients who have more medical complications have higher emergency room utilization rates than patients with less complicated medical history and that certain comorbidities (hyperlipidemia, hypertension and chronic obstructive pulmonary disease) may be predictors of emergency room utilization. These trends may allow providers to create more specialized treatment and care plans for patients at greater risk of emergency room utilization.Signature Page / Title Page / Abstract / Table of Contents / List of Figures / List of Tables / List of Appendices / Introduction to Cancer and its Treatment / Introduction to the Patient Centered Medical Home Model / Treatment of Cancer in Alaska / Study Goals, Rationale, Research Questions and Hypotheses / Methods / Sample Demographics and Description / Results and Discussion / Strengths and Limitations / Future Directions / References / Appendice

A Consent-based Workflow System for Healthcare Systems

In this paper, we describe a new framework for healthcare systems where patients are able to control the disclosure of their medical data. In our framework, the patient's consent has a pivotal role in granting or removing access rights to subjects accessing patient's medical data. Depending on the context in which the access is being executed, different consent policies can be applied. Context is expressed in terms of workflows. The execution of a task in a given workflow carries the necessary information to infer whether the consent can be implicitly retrieved or should be explicitly requested from a patient. However, patients are always able to enforce their own decisions and withdraw consent if necessary. Additionally, the use of workflows enables us to apply the need-to-know principle. Even when the patient's consent is obtained, a subject should access medical data only if it is required by the actual situation. For example, if the subject is assigned to the execution of a medical diagnosis workflow requiring access to the patient's medical record. We also provide a complex medical case study to highlight the design principles behind our framework. Finally, the implementation of the framework is outlined

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Personalizing Situated Workflows for Pervasive Healthcare Applications

In this paper, we present an approach where a workflow system is combined with a policy-based framework for the specification and enforcement of policies for healthcare applications. In our approach, workflows are used to capture entitiespsila responsibilities and to assist entities in fulfilling them. The policy-based framework allows us to express authorisation policies to define the rights that entities have in the system, and event-condition-action (ECA) policies that are used to adapt the system to the actual situation. Authorisations will often depend on the context in which patientspsila care takes place, and our policies support predicates that reflect the environment. ECA policies capture events that reflect the current state of the environment and can perform actions to accordingly adapt the workflow execution. We show how the approach can be used for the Edema treatment and how fine-grained authorisation and ECA policies are expressed and used

An intelligent information forwarder for healthcare big data systems with distributed wearable sensors

© 2016 IEEE. An increasing number of the elderly population wish to live an independent lifestyle, rather than rely on intrusive care programmes. A big data solution is presented using wearable sensors capable of carrying out continuous monitoring of the elderly, alerting the relevant caregivers when necessary and forwarding pertinent information to a big data system for analysis. A challenge for such a solution is the development of context-awareness through the multidimensional, dynamic and nonlinear sensor readings that have a weak correlation with observable human behaviours and health conditions. To address this challenge, a wearable sensor system with an intelligent data forwarder is discussed in this paper. The forwarder adopts a Hidden Markov Model for human behaviour recognition. Locality sensitive hashing is proposed as an efficient mechanism to learn sensor patterns. A prototype solution is implemented to monitor health conditions of dispersed users. It is shown that the intelligent forwarders can provide the remote sensors with context-awareness. They transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage. The system functions unobtrusively, whilst giving the users peace of mind in the knowledge that their safety is being monitored and analysed