Digital technology plays an increasingly important role in addressing the challenges faced by health and care services such as rising costs, changing demographics, shortage of healthcare professionals. eHealth is the use of information and communication technologies (ICT) for healthcare systems which helps patients and healthcare providers work together to ensure faster, safer and better care. eHealth strengthen the use of ICT in health development through a range of services or systems including electronic health record, clinical decision support system, health informatics, self-monitoring healthcare devices, personalized medicine. This paper presents an eHealth infrastructure for E-mental health which is under development. The infrastructure is being designed to provide internet based interventions and support for interoperability and data analysis.
Today's vast amount of medical data need to be integrated and accessed
intelligently to support better healthcare delivery. Interoperability in healthcare can
bring together partners and facilitate knowledge sharing which can potentially
create new networks of knowledge. Delivering context relevant clinical
information enables decision making through healthcare data analysis. By measuring
and monitoring processes digitally, we can compare data more easily. Such
insight facilitates streamlined work ows, greater e ciency and improved patient
care. Systematic analysis of healthcare data can help to detect patterns so that
healthcare providers can optimize their resource allocation and clinicians can
conduct treatments to individuals and project better health outcomes.
According to WHO a mental health information system should enable managers and
service providers to make well-informed decisions that improve the quality of
care [
In this paper, we give an overview of an E-mental health infrastructure that
facilitates the development of mental health information systems. In many
developed countries, majority of their citizens use public healthcare services. To
support a variety of healthcare service providers, these healthcare systems
often use a large number of software applications. For collecting and processing
healthcare data from various sources we require healthcare interoperability. The
term `interoperability' refers to the ability of di erent information systems to
exchange information and understand the semantic of information. Healthcare
interoperability is very essential to reduce the processing time that is required
for the conversions of healthcare information originated by di erent healthcare
providers and/or systems. It is considered as one of the biggest challenge in
today's healthcare systems due to the fact that healthcare systems are
inherently complex and there are many players involved in the healthcare industry.
There has been a lot of initiatives to address healthcare interoperability over
the past decade. HL7 [
There is a great need for doing research in health service improvement to provide the best care possible to the patients with limited resources. Research related to health service improvement needs to undertake many complex tasks such as root cause analysis, capture information from previous steps into a simple document and study the variability of a large number of patient population. It is challenging to accomplish these tasks and therefore we require techniques and tool support. For the examination and study of healthcare information we use data mining techniques. Data mining techniques provide deeper insights into patients health by analyzing historical healthcare information including patients diet, appointments, exercise, lab results, vital signs, prescriptions, treatments, allergies, etc. Data mining techniques such as process mining in healthcare brings the opportunity to learn from patients healthcare information including children, women, elderly, patients with co-morbidity and the results can be utilized for optimizing healthcare resources and the improvement of health service delivery.
We need to disseminate the analysis results to a diverse group of people in the healthcare system. Healthcare managers, analysts and clinicians need to visualize healthcare processes across disciplines to investigate the common pathways of patients. Identifying common pathways for patients ow in healthcare systems is complex as we need to deal with a variety of patients group. While analyzing common pathways for patients, di erent context need to be setup to focus on different group of patients and visualize their care ows. For instance, the manager of the pediatrics department in a hospital would be interested to look at the ow of patients' admission at the children clinics and other departments to make a better planning of resource distribution; a clinician would be interested to investigate the common pathways of patients with mental and behavioral disorders to extract knowledge about concurrent common mental disorders; an analyst may be interested to investigate the e ciency of a new planning strategy. Resources in healthcare may include time, money, facilities, equipment, people and competences. Proper resource planning needs to be carried out in healthcare to ensure that healthcare providers are not overloaded with work, patients are not waiting too long to get services, and the overall cost of healthcare is optimized. In our approach, we disseminate the results of care ow analysis to healthcare professionals for service quality improvement. 2
There are a variety of health service providers in a healthcare system and the
healthcare data are often siloed away from other data. Data silos is not only the
problem in a healthcare setting but di erent standards are being followed by
di erent health facilities to code diagnosis, lab test results, medical procedures
and drugs. Variety of data models of di erent service providers are making data
analysis challenging. To address this issue, it is therefore important to create
an infrastructure for ad-hoc exploration of large collections of data. Such an
infrastructure needs to be exible and scalable yet supporting suitable format for
decision making. We envision a healthcare information system that provides
access to information from various healthcare providers as well as patients personal
healthcare devices and/or applications. Availability of information from patients
personal healthcare devices can potentially be used to detect complicated
problems correctly in their early stage and monitor the e ects of treatment. For
instance, bipolar disorder can be di cult to diagnose and according to a study
published in Psychiatry, around 69 percent of bipolar disorder cases are
misdiagnosed [
We chose to use HL7 FHIR as it allows us to integrate healthcare information collected from several sources. Although HL7 FHIR provides a suitable way to harmonize healthcare information, it does not provide any sophisticated visualization technique to get an overview of patients health or administrative information. In our approach, we apply process mining techniques for extracting an overall picture of healthcare information from various contextual view and from di erent level of abstraction and utilize machine learning techniques to constantly monitor patients condition and raise alarms.
Mobile and VR apps Wellness and fitness
devices Remote monitoring devices s n o i tsn reca iltca
Process mining
output Machine learning
Figure 1 illustrates an E-mental health system architecture where a FHIR database is used to store the data captured from healthcare service providers as well as patients personal healthcare devices and applications. A care ow analysis tool is used to perform data analysis including process mining and machine learning. The care ow analyzer prepares the event logs by querying the FHIR database. Security and privacy are major concerns for healthcare systems. Different types of users may be involved in the process mining related work. The users must have proper authorization to access patients healthcare information. It might be possible to give partial access to the healthcare information stored in the FHIR database. A FHIR interceptor is incorporated in the system architecture to handle users authorization to access FHIR resources. The interceptor will intercept all the FHIR queries and consults with an identity and access management module and returns data that the user is authorized to access.
We have developed applications to provide digital interventions for some
clinical cases such as managing depression, monitoring bipolar patients status,
treatment for social anxiety disorder [
Typically, a process model describes the activities needed to be performed within
a given process by di erent actors such as physicians, nurses, and lab technicians.
Therefore, mining a process would in general extract a process model
representing the activities being performed in a healthcare system. The primary focus of
process mining in healthcare is to provide evidence-based process analysis
techniques for e ective process management [
Existing process mining tools or techniques [
Ontologies are often used to standardize terminologies in healthcare. For
example, the ICD-10 (International Classi cation of Diseases) ontology is designed
to provide diagnostic codes for classifying diseases, including wide variety of
signs, symptoms, abnormal ndings, etc. The SNOMED CT [
Orthopedic Dermatology Care Plan
Episode
Of Care
Healthcare Observation
Procedure
Gynocology
Psychiatry Clinical finding
HL7 FHIR resources EpisodeOfCare
Observation
Procedure Clinical findings regarding mental and behavioral disorders
Condition
CarePlan by healthcare professionals worldwide and its use has improved the quality of medical health records by providing consistency in using medical terms. Ontologies can be used to de ne suitable level of abstraction for selecting a particular patient group and for visualizing care- ow from a high level of abstraction. We intend to provide a customizable framework where domain ontologies such as ontologies for care-plans, symptoms can be easily constructed and attached to the data source. 3.2
The concept of dimensional modeling originated from data warehousing and
business intelligence (DW/BI) [
The establishment of any large IT infrastructure for healthcare on a regional,
national or international level governs by political in uences. The Norwegian
Center for E-health Research recently published a report [
This work is partially supported by The Research Council of Norway as a part of the INTROducing Mental health through Adaptive Technology (INTROMAT) project under grant agreement 259293.