Electronic prescription systems in Greece: a large-scale survey of healthcare professionals' perceptions.

BACKGROUND: The national e-prescription system in Greece is one of the most important achievements in the e-health sector. Healthcare professionals' feedback is essential to ensure the introduced system tends to their needs and reduces their everyday workload. The number of surveys collecting the users' views is limited, while the existing studies include only a small number of participants. METHODS: In this study, healthcare professionals' perceptions on e-prescription are explored. For this, a questionnaire was distributed online, containing closed- and open-ended questions aiming to address strengths and identify drawbacks in e-prescription. Answers were collected from primary health care physicians, specialized medical doctors and pharmacists. RESULTS: In total, 430 answers were collected (129 from primary health care physicians, 164 responses from specialized medical doctors and 137 pharmacists). Analysis of the collected answers reveals that the views of the three groups of healthcare professionals mostly converge. The positive impact e-prescribing systems have on the overall prescribing procedure in preventing errors and providing automation is commented. Among gaps identified and proposed improvements, health care professionals note the need for access to information on adverse drug reactions, side effects, drug-to-drug interactions and allergies. Flexible interaction with Therapeutic Prescription Protocols is desired to ameliorate monitoring and decision-making, while drug dosing features, and simplified procedures for copying, repeating, canceling a prescription, are perceived as useful to incorporate. CONCLUSIONS: Collecting healthcare professionals' feedback is important, as their views can be transcribed to system requirements, to further promote e-prescribing and improve the provided health care services by facilitating decision making through safer and more efficient e-prescription. Introduction of the identified improvements can simplify the everyday workflow of healthcare professionals. To the best of our knowledge, a survey with more than 400 answered questionnaires on the use of e-prescription systems by healthcare professionals has never been conducted in Greece before.

Evaluation of an electronic prescription platform: Clinicians' feedback on three distinct services aiming to facilitate clinical decision and safer e-prescription.

BACKGROUND: Health Care Professionals (HCPs) are the main end-users of digital clinical tools such as electronic prescription systems. For this reason, it is of high importance to include HCPs throughout the design, development and evaluation of a newly introduced system to ensure its usefulness, as well as confirm that it tends to their needs and can be integrated in their everyday clinical practice. METHODS: In the context of the PrescIT project, an electronic prescription platform with three services was developed (i.e., Prescription Check, Prescription Suggestion, Therapeutic Prescription Monitoring). To allow an iterative process of discovery through user feedback, design and implementation, a two-phase evaluation was carried out, with the participation of HCPs from three hospitals in Northern Greece. The two-phase evaluation included presentations of the platform, followed by think-aloud sessions, individual platform testing and the collection of qualitative as well as quantitative feedback, through standard questionnaires (e.g., SUS, PSSUQ). RESULTS: Twenty one HCPs (8 in the first, 18 in the second phase, and five present in both) participated in the two-phase evaluation. HCPs comprised clinicians varying in their specialty and one pharmacist. Clinicians' feedback during the first evaluation phase already deemed usability as "excellent" (with SUS scores ranging from 75 to 95/100, showing a mean value of 86.6 and SD of 9.2) but also provided additional user requirements, which further shaped and improved the services. In the second evaluation phase, clinicians explored the system's usability, and identified the services' strengths and weaknesses. Clinicians perceived the platform as useful, as it provides information on potential adverse drug reactions, drug-to-drug interactions and suggests medications that are compatible with patients' comorbidities and current medication. CONCLUSIONS: The developed PrescIT platform aims to increase overall safety and effectiveness of healthcare services. Therefore, including clinicians in a two-phase evaluation confirmed that the introduced system is useful, tends to the users' needs, does not create fatigue and can be integrated in their everyday clinical practice to support clinical decision and e-prescribing.

A Sustainable Model for Healthcare Systems: The Innovative Approach of ESG and Digital Transformation.

In recent years, the globe has faced a series of topics of growing concern, such as the COVID-19 pandemic, the international financial crisis, rising socio-economic inequalities, the negative outcomes of greenhouse gas emissions, which resulted in climate change, and many others. Organizations worldwide have confronted these new challenges of sustainable finance by incorporating environmental, social, and corporate governance (ESG) factors and digital transformation (DT) in their innovation business strategies. The healthcare sector represents a large share of the global economy (about 10% of global economic output), employs a large number of workers, and needs to rely more on an open innovation model where interested parties, especially patients, are going to have a say in their own well-being. Thus, it is imperative that healthcare providers be efficient, effective, resilient, and sustainable in the face of significant challenges and risks. At the same time, they must offer sustainable development goals and digital transformation to healthcare users through limited governmental resources. This study investigates the role, importance, and correlation of ESG factors and digital transformation to the sustainable finance of healthcare systems through an innovative model. The main purpose of the paper is to present the already implemented ESG and DT factors in the healthcare sector and to propose a mutual and combined implementation strategy based on common evaluation tools, methods, and actions. A set of proposed actions and strategies are presented for the sustainability and resilience of the healthcare sector.

A hackathon as a tool to enhance research and practice on electronic health record systems' interoperability for chronic disease management and prevention.

Objectives: The development of a standardized technical framework for exchanging electronic health records is widely recognized as a challenging endeavor that necessitates appropriate technological, semantic, organizational, and legal interventions to support the continuity of health and care. In this context, this study delineates a pan-European hackathon aimed at evaluating the efforts undertaken by member states of the European Union to develop a European electronic health record exchange format. This format is intended to facilitate secure cross-border healthcare and optimize service delivery to citizens, paving the way toward a unified European health data space. Methods: The hackathon was conducted within the scope of the X-eHealth project. Interested parties were initially presented with a representative clinical scenario and a set of specifications pertaining to the European electronic health record exchange format, encompassing Laboratory Results Reports, Medical Imaging and Reports, and Hospital Discharge Reports. In addition, five onboarding webinars and two professional training events were organized to support the participating entities. To ensure a minimum acceptable quality threshold, a set of inclusion criteria for participants was outlined for the interested teams. Results: Eight teams participated in the hackathon, showcasing state-of-the-art applications. These teams utilized technologies such as Health Level Seven-Fast Healthcare Interoperability Resources (HL7 FHIR) and Clinical Document Architecture (CDA), alongside pertinent IHE integration profiles. They demonstrated a range of complementary uses and practices, contributing substantial inputs toward the development of future-proof electronic health record management systems. Conclusions: The execution of the hackathon demonstrated the efficacy of such approaches in uniting teams from diverse backgrounds to develop state-of-the-art applications. The outcomes produced by the event serve as proof-of-concept demonstrators for managing and preventing chronic diseases, delivering value to citizens, companies, and the research community.

Greek Hospital Data Mining and Analysis.

Monitoring the performance of hospitals is a crucial issue related both with the quality of healthcare services and with country's economy. An easy and trustful way of evaluating health systems is through key performance indicators (KPIs). Such indicators are widely used for the identification of gaps in the quality or efficiency of the services provided. The main aim of this study is the analysis of the financial and operational indicators at hospitals in the 3rd and 5th Healthcare Regions of Greece. In addition, through cluster analysis and data visualization we attempt to uncover hidden patterns that may lie within our data. The results of the study support the need for re-evaluation of the assessment methodology of Greek hospitals to identify the weaknesses in the system, while evidently unsupervised learning exposes the potential of group-based decision making.

The PrescIT Knowledge Graph: Supporting ePrescription to Prevent Adverse Drug Reactions.

Adverse Drug Reactions (ADRs) are an important public health issue as they can impose significant health and monetary burdens. This paper presents the engineering and use case of a Knowledge Graph, supporting the prevention of ADRs as part of a Clinical Decision Support System (CDSS) developed in the context of the PrescIT project. The presented PrescIT Knowledge Graph is built upon Semantic Web technologies namely the Resource Description Framework (RDF), and integrates widely relevant data sources and ontologies, i.e., DrugBank, SemMedDB, OpenPVSignal Knowledge Graph and DINTO, resulting in a lightweight and self-contained data source for evidence-based ADRs identification.

Atlantes: Automated Health Related & COVID-19 Data Management for Use in Predictive Models.

The scientific community, having turned its interest, almost entirely, to the treatment and understanding of COVID-19, is constantly striving to collect and use data from the countless available sources. That data, however, is scattered, not designed to be combined, collected in different time periods and their volume is constantly increasing. In this paper, we present an automated methodology that collects, refines, groups and combines data for a large number of countries. Most of these data resources are directly related to COVID-19 but we also choose to include other types of variables for each country, which may be of particular interest for researchers working in understanding the COVID-19 pandemic. The presented methodology unifies critical information regarding the pandemic. It is implemented in Python, provided as a simple script that extracts data, in the form of a daily time series, in a short period of time, directly available to be incorporated for analysis.

Using Business Process Management Notation to Model Therapeutic Prescription Protocols: The PrescIT Approach.

Clinical Decision Support Systems (CDSS) could play a prominent role in preventing Adverse Drug Reactions (ADRs) especially when integrated in larger healthcare systems (e.g. Electronic Health Record - EHR systems, Hospital Management Systems - HMS, e-Prescription systems etc.). This poster presents an approach to model Therapeutic Prescription Protocols (TPPs) via the Business Process Management Notation (BPMN), as part of the e-Prescription CDSS developed in the context of the PrescIT project.

An Integrated Platform for Skin Cancer Heterogenous and Multilayered Data Management.

Electronic health record (EHR) systems improve health care services by allowing the combination of health data with clinical decision support features and clinical image analyses. This study presents a modular and distributed platform that is able to integrate and accommodate heterogeneous, multidimensional (omics, histological images and clinical) data for the multi-angled portrayal and management of skin cancer patients. The proposed design offers a layered analytical framework as an expansion of current EHR systems, which can integrate high-volume molecular -omics data, imaging data, as well as relevant clinical observations. We present a case study in the field of dermatology, where we attempt to combine the multilayered information for the early detection and characterization of melanoma. The specific architecture aspires to lower the barrier for the introduction of personalized therapeutic approaches, towards precision medicine. The paper describes the technical issues of implementation, along with an initial evaluation of the system and discussion.

Electronic registry for the management of childhood obesity in Greece.

BACKGROUND: Obesity in childhood and adolescence represents a major health problem in our century. In Greece, more than 30%-35% of children and adolescents are either overweight or obese. MATERIALS AND METHODS: Using information and communication technologies, we developed a "National Registry for the Prevention and Management of Overweight and Obesity in Childhood and Adolescence" for guidance and training of Pediatricians and General Practitioners. The application supports interoperability with other national infrastructures and multi-layered security spanning preventive, detective and administrative controls. The Patient Summary Dataset includes information on medical history, family history, medications, immunizations, clinical examination and laboratory findings and appointment booking service. RESULTS: The application was launched in September 2015 and is accessible by: http://app.childhood-obesity.gr/. Based on the data that the doctor registers, the system calculates a personalized therapeutic algorithm that provides information on diet, physical exercise and sleep, as well as guidance on laboratory investigations and referral to specialized centres. A pilot study performed in 1270 children and adolescents indicated that using this system resulted in a reduction in obesity rates by 30% and overweight rates by 35% within 1 year. CONCLUSIONS: This National e-Health System appears to be effective in the management of overweight and obesity in childhood and adolescence.

The impact of economic crisis on the Greek hospitals' productivity.

During the recent economic crisis, Greece implemented a comprehensive reform in the health care system. The 2010 health reform occurred under the constraints imposed by the memorandum of understanding that the Greek Government signed with its EU/International Monetary Fund creditors to control its deficit. The objective of the study is to examine the impact of the reform on the efficiency and productivity of public hospitals in Greece. We use the Malmquist productivity index to comparatively examine the potential changes before and after the reform years. We compare productivity, efficiency, and technological changes using panel data of 111 public acute hospitals operating in Greece throughout the recession period of 2009 to 2012. Bootstrapping methods are applied to allow for uncertainty owing to sampling error and for statistical inference for the Malmquist productivity index and its decompositions. The analysis indicates that the productivity has been increased following the policy changes. It appears that the expected benefits from the reform in general have been achieved, at least in the short-term. This result is examined in the light of management and operations activities, which are related with the reform process. Therefore, at a second stage, the Malmquist index is regressed on variables that may potentially be statistically associated with productivity growth.

Making sense of big data in health research: Towards an EU action plan.

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans.

Reforming reimbursement of public hospitals in Greece during the economic crisis: Implementation of a DRG system.

OBJECTIVES: Until recently, in-patient NHS hospital care in Greece was reimbursed via an anachronistic and under-priced retrospective per diem system, which has been held primarily responsible for continuous budget deficits. The purpose of this paper is to present the efforts of the Ministry of Health (MoH) to implement a new DRG-based payment system. METHODS: As in many countries, the decision was to adopt a patient classification from abroad and to refine it for use in Greece with national data. Pricing was achieved with a combination of activity-based costing with data from selected Greek hospitals, and "imported" cost weights. Data collection, IT support and monitoring are provided via ESY.net, a web-based facility developed and implemented by the MoH. RESULTS: After an initial pilot testing of the classification in 20 hospitals, complete DRG reimbursement data was reported by 113 hospitals (85% of total) for the fourth quarter of 2011. The recorded monthly increase in patient discharges billed with the new system and in revenue implies increasing adaptability by the hospitals. However, the unfavorable inlier vs. outlier distribution of discharges and revenue observed in some health regions signifies the need for corrective actions. CONCLUSIONS: The importance of this reimbursement reform is discussed in light of the current crisis faced by the Greek economy. There is yet much to be done and many projects are currently in progress to support this effort; however the first cost containment results are encouraging.

A pattern similarity scheme for medical image retrieval.

In this paper, we propose a novel scheme for efficient content-based medical image retrieval, formalized according to the PAtterns for Next generation DAtabase systems (PANDA) framework for pattern representation and management. The proposed scheme involves block-based low-level feature extraction from images followed by the clustering of the feature space to form higher-level, semantically meaningful patterns. The clustering of the feature space is realized by an expectation-maximization algorithm that uses an iterative approach to automatically determine the number of clusters. Then, the 2-component property of PANDA is exploited: the similarity between two clusters is estimated as a function of the similarity of both their structures and the measure components. Experiments were performed on a large set of reference radiographic images, using different kinds of features to encode the low-level image content. Through this experimentation, it is shown that the proposed scheme can be efficiently and effectively applied for medical image retrieval from large databases, providing unsupervised semantic interpretation of the results, which can be further extended by knowledge representation methodologies.