Timur Beyan

Today, with the technology-driven developments, healthcare systems and services are being radically transformed to become    more effective and efficient. Omics technologies along with mobile sensors and monitoring systems are emerging disruptive technologies, which will provide us the opportunities of a paradigm shifting in medical theory, research and practice. Traditional methods are beginning to convert to a new personalized, predictive, preventive and participatory paradigm based on big data approaches.We anticipate that; next-generation health information systems will be constructed based on tracking all aspects of health status on 24/7, and returning evidence based recommendations to empower individuals. As an example of future personal health record (PHR) concept, GO-WELL is based on clinical envirogenomic knowledge base (CENG-KB) to engage patients for predictive care. In this chapter, we present the design principles of this system, after describing several concepts, including personalized medicine, omics revolution, incorporation of genomic data into medical decision processes, and the utilization of enviro-behavioural parameters for disease risk assessment.

Background: Personalized medicine approaches provide opportunities for predictive and preventive medicine. Using genomic, clinical, environmental, and behavioral data, tracking and management of individual wellness is possible. A prolific way to carry this personalized approach into routine practices can be accomplished by integrating clinical interpretations of genomic variations into electronic medical records (EMRs)/electronic health records (EHRs). Today, various central EHR infrastructures have been constituted in many countries of the world including Turkey.
Objective: The objective of this study was to concentrate on incorporating the personal single nucleotide polymorphism (SNP) data into the National Health Information System of Turkey (NHIS-T) for disease risk assessment, and evaluate the performance of various predictive models for prostate cancer cases. We present our work as a miniseries containing three parts: (1) an overview of requirements, (2) the incorporation of SNP into the NHIS-T, and (3) an evaluation of SNP incorporated NHIS-T for prostate cancer.
Methods: For the first article of this miniseries, the scientific literature is reviewed and the requirements of SNP data integration into EMRs/EHRs are extracted and presented.
Results: In the literature, basic requirements of genomic-enabled EMRs/EHRs are listed as incorporating genotype data and its clinical interpretation into EMRs/EHRs, developing accurate and accessible clinicogenomic interpretation resources (knowledge bases), interpreting and reinterpreting of variant data, and immersing of clinicogenomic information into the medical decision processes. In this section, we have analyzed these requirements under the subtitles of terminology standards, interoperability standards, clinicogenomic knowledge bases, defining clinical significance, and clinicogenomic decision support.
Conclusions: In order to integrate structured genotype and phenotype data into any system, there is a need to determine data components, terminology standards, and identifiers of clinicogenomic information. Also, we need to determine interoperability standards to share information between different information systems of stakeholders, and develop decision support capability to interpret genomic variations based on the knowledge bases via different assessment approaches.

Background: A personalized medicine approach provides opportunities for predictive and preventive medicine. Using genomic, clinical, environmental, and behavioral data, the tracking and management of individual wellness is possible. A prolific way to carry this personalized approach into routine practices can be accomplished by integrating clinical interpretations of genomic variations into electronic medical record (EMR)s/electronic health record (EHR)s systems. Today, various central EHR infrastructures have been constituted in many countries of the world, including Turkey.
Objective: As an initial attempt to develop a sophisticated infrastructure, we have concentrated on incorporating the personal single nucleotide polymorphism (SNP) data into the National Health Information System of Turkey (NHIS-T) for disease risk assessment, and evaluated the performance of various predictive models for prostate cancer cases. We present our work as a miniseries containing three parts: (1) an overview of requirements, (2) the incorporation of SNP into the NHIS-T, and (3) an evaluation of SNP data incorporated into the NHIS-T for prostate cancer.
Methods: For the second article of this miniseries, we have analyzed the existing NHIS-T and proposed the possible extensional architectures. In light of the literature survey and characteristics of NHIS-T, we have proposed and argued opportunities and obstacles for a SNP incorporated NHIS-T. A prototype with complementary capabilities (knowledge base and end-user applications) for these architectures has been designed and developed.
Results: In the proposed architectures, the clinically relevant personal SNP (CR-SNP) and clinicogenomic associations are
shared between central repositories and end-users via the NHIS-T infrastructure. To produce these files, we need to develop a national level clinicogenomic knowledge base. Regarding clinicogenomic decision support, we planned to complete interpretation of these associations on the end-user applications. This approach gives us the flexibility to add/update envirobehavioral parameters and family health history that will be monitored or collected by end users.
Conclusions: Our results emphasized that even though the existing NHIS-T messaging infrastructure supports the integration of SNP data and clinicogenomic association, it is critical to develop a national level, accredited knowledge base and better end-user systems for the interpretation of genomic, clinical, and envirobehavioral parameters.

Background: A personalized medicine approach provides opportunities for predictive and preventive medicine. Using genomic, clinical, environmental, and behavioral data, the tracking and management of individual wellness is possible. A prolific way to carry this personalized approach into routine practices can be accomplished by integrating clinical interpretations of genomic variations into electronic medical records (EMRs)/electronic health records (EHRs). Today, various central EHR infrastructures have been constituted in many countries of the world, including Turkey.
Objective: As an initial attempt to develop a sophisticated infrastructure, we have concentrated on incorporating the personal single nucleotide polymorphism (SNP) data into the National Health Information System of Turkey (NHIS-T) for disease risk assessment, and evaluated the performance of various predictive models for prostate cancer cases. We present our work as a three part miniseries: (1) an overview of requirements, (2) the incorporation of SNP data into the NHIS-T, and (3) an evaluation of SNP data incorporated into the NHIS-T for prostate cancer.
Methods: In the third article of this miniseries, we have evaluated the proposed complementary capabilities (ie, knowledge base and end-user application) with real data. Before the evaluation phase, clinicogenomic associations about increased prostate cancer risk were extracted from knowledge sources, and published predictive genomic models assessing individual prostate cancer risk were collected. To evaluate complementary capabilities, we also gathered personal SNP data of four prostate cancer cases and fifteen controls. Using these data files, we compared various independent and model-based, prostate cancer risk assessment approaches.
Results: Through the extraction and selection processes of SNP-prostate cancer risk associations, we collected 209 independent associations for increased risk of prostate cancer from the studied knowledge sources. Also, we gathered six cumulative models and two probabilistic models. Cumulative models and assessment of independent associations did not have impressive results. There was one of the probabilistic, model-based interpretation that was successful compared to the others. In envirobehavioral and clinical evaluations, we found that some of the comorbidities, especially, would be useful to evaluate disease risk. Even though we had a very limited dataset, a comparison of performances of different disease models and their implementation with real data as use case scenarios helped us to gain deeper insight into the proposed architecture.
Conclusions: In order to benefit from genomic variation data, existing EHR/EMR systems must be constructed with the capability of tracking and monitoring all aspects of personal health status (genomic, clinical, environmental, etc) in 24/7 situations, and also with the capability of suggesting evidence-based recommendations. A national-level, accredited knowledge base is a top requirement for improved end-user systems interpreting these parameters. Finally, categorization using similar, individual characteristics (SNP patterns, exposure history, etc) may be an effective way to predict disease risks, but this approach needs to be concretized and supported with new studies.

Abstract: Main components of quality in healthcare can be named as effectiveness, efficiency, equity, patient safety, patient centeredness and timeliness. Supply and clinical delivery chain of drug and medical materials, as important parts of health care delivery, dramatically affects patient safety and efficiency. In order to achieve more effective and safer health care delivery, new approaches and technologies have been developed for various stages and levels of health care supply chain. Transformation of the system requires the development of new standards and modification of processes. That transformation has special requirements for the health care systems that composed of many health care deliverers with a central procurement / logistic. In this study, we have examined health care supply chain from the quality of care perspective, examined practices from the World and Turkey, and present a framework proposal for the transforming hospitals and health care systems.

Özet:
Tıbbi bakımda kalitenin temel bileşenleri arasında etkililik, verimlilik, eşitlik, hasta güvenliği, hasta merkezlilik ve zamanlılık sayılabilir. Sağlık hizmet sunumunun ana unsurlarından olan ilaç ve tıbbi malzemenin tedarik ve klinik zinciri özellikle verimlilik ve hasta güvenliğine doğrudan etki etmektedir. Daha verimli işletmeler ve daha güvenli sağlık hizmeti için sağlık tedarik zincirinin çeşitli evre ve düzeylerinde yeni yaklaşımlar ve teknolojiler geliştirilmektedir. Mevcut yapıdan yeni bir sisteme geçiş peşi sıra yeni standart ve süreçlerin de değişimini getirmektedir. Söz konusu geçiş özellikle çok sayıda sağlık hizmet sunucundan oluşan ve merkezi tedarik yapıları olan sistemlerde ayrı özellikler taşımaktadır. Sağlık tedarik zincirinin kaliteli tıbbi bakım sunumu perspektifinden dünyadaki ve Türkiye’deki örnek ve uygulamaları ile incelendiği bu çalışmada hastane ve tıbbi bakım sistemlerine dönüşüm sürecinde kullanılabilecek bir çerçeve önerisi sunulmaktadır.

Abstract: Performance measurement is a core for all health care systems in transition. The objective of the research is to develop an ontological framework to represent performance measurement domain to facilitate information infrastructure and apply this ontology to semantically interpret the performance measurement studies. In the scope of study OWL and SWRL based ontology is developed and a knowledge base is formed for performance measurement studies. The developed tool is applied in Turkish system for evaluation.

Özet: Dönüşen sağlık sistemlerinde performans ölçüm ve değerlendirmesinin önemi giderek artmaktadır. Bu çalışmanın amacı, bu alanında geliştirilecek bilgi teknolojileri için performans ölçümü çalışmaların anlamsal olarak yorumlanmasını sağlayacak bir altyapı oluşturmaktır. OWL ve SWRL dilleri kullanılarak bir ontoloji oluşturulmuş, ve dünya literatüründen elde edilen performans ölçümü olguları bu ontoloji kullanılarak bilgi tabanına çevrilmiştir. Çalışmanın sonunda elde edilen ontoloji ve bilgi tabanı Türkiye sistemine uygulanarak değerlendirilmiştir.

Sağlık bakımı alanında performans ölçütlerinin belirlenmesi ve değerlendirilmesinde kabul görmüş genelgeçer bir model bulunmamaktadır. Her ülke kendi sağlık sunum sistemlerinin farklı kısımlarını farklı amaçlarla izlemektedir. Ayrıca, birbirinden oldukça ayrımlaşmış sağlık sunumu sistemlerinde uygulanan performans ölçüm çalışmalarını karşılaştırmayı sağlayan teorik bir çerçeve de bulunmamaktadır. Bu çalışmanın amacı, farklı bireysel ve kurumsal performans modellerini karşılaştırmak için özgün bir teorik çerçeve önermektir. Araştırma kapsamında sağlık bakım sistemlerinde performans ölçümü alanında 2000 yılı ve sonrasında yayımlanan 229 makale incelenmiştir. Seçilen makaleler, çalışmaların veri kaynağı, kesintisiz sağlık bakımı zincirindeki konumu, sağlık sisteminde hedef aldığı  organizasyonel düzey , uygulandığı bakım durumları ve hastalıklar, sağlık sunumcusunda ölçümün odaklandığı katman,
hedeflenen iyileştirmeler, ölçülen göstergeler ve göstergelerinin türleri açısından sınıflandırılmıştır. Ayrıca, performans ölçümü çalışmaları, uygulandıkları sağlık bakım sunumu sisteminin nitelikleri ve paydaşları açısından da ele alınmıştır. Çalışmanın
sonucunda, farklı performans ölçüm yöntemlerini dünyadaki benzer örneklerle karşılaştırma imkanı sağlayan çok boyutlu bir araç geliştirilmiştir. Bu araç Türkiye’de Sağlık Bakanlığı tarafından yürütülen performans ölçüm çalışmalarına uygulanmış ve dünya örnekleri ile karşılaştırmalı olarak incelenmiş, dünyadaki çalışmaları yakalayan boyutları tespit edilmiş ve dünyadaki örneklere referansla geliştirilebilecek yeni
boyutlar önerilmiştir.

Sağlık sistemi ve sağlık bakım sistemi çağımızda yaygın olarak mekanistik bakış açıları ile ele alınmakta ve değerlendirilmektedir. Sisteme yönelik sorunlar ve çözüm önerileri de bu bakış açısının dışavurumları olmaktadır. Ancak, sağlık sistemi ve alt sistemleri mekanik unsur olmaktan ziyade canlı bir organizama gibi davranmaktadırlar. Bu anlamda, yeni bir araştırma alanı olan kompleks adaptif sistemler yaklaşımı, sağlık sisteminin modellenmesi, incelenmesi, anlaşılması ve sorunlarına çözümler geliştirilmesinde katkı sağlayıcı olacaktır. Bu makalede, genel anlamıyla kompleks adaptif sistemler yaklaşımı ve bunun sağlık sisteminin yönetsel ve performansa ilişkin boyutlarına katkıları ele alınmıştır.

Background: The quality of health information on the Internet is extremely variable, limiting its use as a serious information source. The studies on the web have been done mostly on sites in English, the sites in other languages are not well assessed.
Objective: To assess the accuracy of health information about
fever in children on Turkish web sites.
Methods: We used Google and some Turkish search engines to reach Web sites related to fever in children. The accuracy of these Web sites has been evaluated.
Results: We determined 41 web sites. Thirty-two (78 %) gave a specific temperature above which a child is considered to have fever. Twenty-seven (66 %) had no explanation on the correct
way to take a child’s temperature. Eighteen (44 %) mentioned drug treatment. Non-drug remedies were indicated in 38 (93 %). Twenty eight (68 %) gave at least some indication of when parents should call a doctor. Two (5%) of the websites had satisfactory information about childhood fever. Six (15 %) web sites included incorrect information.
Conclusions: The quality of Turkish health information on the Internet is questionable, and some of the websites can misdirect users.