Introduction to health informatics
August 20, 20201.0 Introduction to Health Informatics
Health IT has been introduced and used in health care settings for many years now, trusting that it will increase the quality and efficiency of health care. Numerous studies and reports have shown that health IT can be associated with an increase in quality, efficiency and safety. Many health information associations have adopted codes of professionals and ethical conduct.Modern healthcare is not thinkable without health IT, especially when taking into account the exploding amount of health information generated by more and more elaborate diagnostic and therapeutic technology, the growing need for communication and cooperation between different health care professional groups and health care institutions when treating multimorbid patients in an aging population, and the challenge of providing high quality care in times of economic crisis. The obligation for health informatics is to respond to these challenges and to provide efficient and effective health IT solutions, with as much benefit and as little negative side effects as possible. To achieve this, health informatics as a discipline must be able to learn, both from its successes as well as from its failures.
Health Informatics
1.1 The Vision of Evidence based Health Informatics
To understand evidence-based health informatics, it is helpful to have a look at the history and motivation of evidence-based medicine.Health informatics strives to improve health care through technical (often socio technical) interventions. Along these lines, it needs to provide evidence for the efficiency and effectiveness of its interventions. These decisions can be, for example, decisions on whether to introduce a certain type of health IT system or not, how to choose among alternative health IT systems, how to customize its user interface, how to introduce it into the clinical work- flow, or how to train it. Some of these decisions will need evidence on efficiency and effectiveness of a health IT intervention; we call this summative evidence. But evidence-based health informatics is not only focused on efficiency and effectiveness: Evidence is also needed to answer questions such as What is the best implementation strategy? or How can unintended consequences be avoided? We can call this formative evidence. This type of evidence helps to improve health IT systems. The term current best evidence implies that scientific findings are needed – and not marketing promises, hopes, or assumptions. Evidence-based health informatics is the conscientious, explicit, and judicious use of current best evidence to support a decision with regard to the selection, implementation, and use of health IT. It means integrating individual health IT expertise with the best available external evidence from systematic research, taking into account the organizational and cultural context of patient care.
1.2 History of Evidence based Health Informatics
While the term evidence-based health informatics has only appeared recently, the understanding that health IT has to be systematically evaluated is quite old. First evaluation studies were already published in the 1970s. An attempt to classify health IT evaluation studies was published in the 1980s. In the 1990s, a more systematic scientific discussion on health IT evaluation started that is reflected e.g. in working conferences of the International Medical Informatics Association (IMIA) in Montpellier (1990), Leiden (1994), and Helsinki (1999). In the 1990s, a more systematic scientific discussion on health IT evaluation started that is reflected e.g. in working conferences of the International Medical Informatics Association (IMIA) in Montpellier (1990), Leiden (1994), and Helsinki (1999) (17). In the following years, books on health IT started to appear [18–24], health IT evaluation was included in many health IT evaluation curricula, health informatics associations (such as IMIA, EFMI, AMIA) started working groups on health IT evaluation [25], and health IT evaluation is now a regular topic at major health informatics conferences. Overall, the number of scientific publications in health informatics has been increasing since the mid-1990s. Even more strongly, the number of system- atic reviews in health informatics has been increasing since 2005.
Figure 1 Number of medical informatics publications (identified by minor or major MeSH term medical informatics in PubMed) for the period 1966 – 2013.
1.3 Evidence and Life Cycle of Health IT
Evidence is generated, as discussed before, by systematic research based on formative or summative evaluation studies. Depending on the life cycle of health IT, evaluation studies have to answer different questions and thus focus on different issues and pro- duce different types of evidence. The following list gives a short, and clearly incomplete, list of possible evaluation questions and methods that can be applied to generate related evidence:
- Development phase: What are the user needs? (needs assessment); Is the software and hardware free of errors? (test runs); Was the software built as defined in the requirements? (verification); Was the software built as wanted by the users? (validation); Will the software work in practice? (simulation studies).
- Pilots and early use: Is the technical quality adequate? (performance measurements); Is the software user- friendly? (usability tests); Is the software sufficiently integrated in the clinical processes? (observations); Does the software work as intended? (interviews)
- Routine use: Is the software adopted as intended? (usage pattern analysis, documentation analysis); Are the users satisfied? (user survey); Is the software cost effective? (cost analysis); Does the soft- ware create errors? (error report analysis); What is the impact of the software on efficiency, appropriateness, organi- zation, or outcome quality of care? (experimental or quasi experimental studies).
Depending on the state within the life cycle, on the type of system, and on the questions that need to be answered, different evaluation studies with different methods will be conducted. It is therefore quite obvious that the full range of quantitative and qualitative evaluation methods that are available, e.g. from biostatistics, epidemiology, social science, psychology, or health care management, can and must be used. From this point of view, any discussion on the “best” evaluation approaches seems somehow misleading.
Each of these individual studies will generate evidence that helps to improve the health IT system or its implementation (formative evidence), or to justify it and decide on its future (summative evidence). This evidence, however, is of primary inter- est for the healthcare organization in which the health IT system is being implemented or operated. We can thus call it “in- house evidence”. To be able to generate evidence that can help other healthcare organizations in taking decisions on a health IT system, we have to aggregate this “in- house evidence”. This is typically done in the form of systematic reviews and meta analysis.
Figure 2 Analysis of the type of addressed evaluation questions of 1,818 health IT evaluation papers published between 1982 and 2014 and contained in the health IT evaluation database. One paper may address more than one evaluation question.
1.4 Towards Evidence – based Health Informatics
Based on what we had achieved, and to make progress towards evidence based health informatics, a well designed evaluation studies needed. These studies need to be published and they need to be locatable for others. They need to be aggre- gated in the form of systematic reviews and meta-analysis. To achieve all this, we need well-trained health informatics specialists. Available evidence then needs to be trans- lated into practice. In addition to studies, we need ad-hoc adverse event reporting systems.
1.5 Challenge in Medical Informatics
1.5.1 Challenge 1 : Quality of Evaluation Studies
Conducting a well-designed evaluation study on health IT has been reported to be challenging. This makes it difficult to control all relevant contextual factors in a controlled or even randomized study de- sign. Health IT is implemented in a steadily changing clinical environment and is itself affected by changes. Different stakeholders often define the “success” of health IT differently. Too many evaluation questions may be of interest (see the life cycle discussion above), only few can be tackled in one study, or evaluation ques- tions may change during the study. To respond to this complexity and to in- crease the internal validity of health IT evaluation studies, several guidelines have been developed.A well-established health IT evaluation guideline was published in 2009 by the Agency for Healthcare Research and Quality (AHRQ) as an “evaluation toolkit”. This toolkit offers step-by-step guid- ance for developing a health IT evaluation plan. This guideline ends with describing how to write an evaluation plan. This tool- kit is frequently used and regularly updated.
1.5.2 Challenge 2 : Publication bias
The next step needed for evidence-based health informatics is to make the in-house evidence that is generated by local evaluation studies available to others. The first step is to publish. In a survey of 136 health IT researchers, we found that a large proportion of health IT evaluation studies, even when being conducted in academic settings, never get published. In this survey, mostly “no time” was given as the reason for non-publication, but political reasons were also mentioned. When studies with negative findings are not published due to political reasons, this is called publication bias. Publication bias is a problem well known in medical research. The registry itself should be maintained by an independent and in- ternational health informatics organization.
1.5.3 Challenge 3 : Training of Health IT Evaluation
In order to make progress towards evidence based health informatics, well- trained health IT evaluation experts are needed.These may have a background in medical informatics, but also in other disciplines such as medicine, nursing, social sciences, health economics, or psychology, depending on the evaluation questions and the methodology used. For health informatics specialists, the International Medical Informatics Association (IMIA) recommends that health IT evaluation form part of the health informatics core curriculum. In particular, these recommendations state that evaluation and assessment of information systems, including study design, selection and triangulation of methods, outcome and impact evaluation, economic evaluation, unintended consequences, systematic reviews and meta analysis, evidence based health informatics should be taught in health informatics curricula. However, a detailed curriculum for health IT evaluation does not exist yet. Therefore, the working groups on health IT evaluation of EFMI, IMIA and AMIA have launched an initiative to develop recom- mendations for health IT evaluation. These recommendations will describe the content of health IT evaluation courses in dependence to the intended level of expertise to be achieved.
1.5.4 Challenge 4 : Reporting Quality of Evaluation Studies
When an evaluation study is published, this publication will be read and used by others to support decisions to be made. The study publication needs to be of sufficiently high quality to support this,but there are many study publications that suffer from insufficient quality. Problems include, among other things: incomplete description of both the technical and the organizational dimension of the health IT interventions; incomplete description of the clinical and organizational setting; incomplete description of the methods or tools used; overoptimistic or uncritical presentation of results; or incomplete dis- cussion of limitations. An analysis on the quality of health IT evaluation papers in the last 15 years showed that their quality remains low. This incomplete information in study publications makes it diffi- cult to use and generalize the evidence of the study.
2.0 Translational Bioinformatics
Translational biomedical informatics is a rapidly emerging discipline to integrate data from medical research, biotechnologies, and electronic medical records, and computational systems medicine is to apply computational and systems biology approaches to solve complex problems in medical research, aiming to improve the diagnosis, prognosis, and treatment of complex diseases. It is also well known that their development needs an integration of mathematical models, statistical methods, and computer algorithms. Complex diseases such as cancers are caused by a combination of genetic, environmental, and lifestyle factors, and thus the research of complex diseases at a system level like gene sets, pathway level, or static/dynamic network is a necessity. The concept of entropy suggests that systems naturally progress from order to disorder. The contributions to the application of this entropy based system to detect cancerous cell nuclei and observe overlapping cellular events occurring during the wound healing process in the human body are also presented.The role of protein structures in understanding diseases becomes more and more important, due to the following two reasons. One is that there are a lot of disease-associated proteins that were discovered, while the other fact is that many diseases are believed to result from misfolded proteins.
Finally, two bioinformatics tools were also involved in this issue. H. Wu et al. contribute a bioinformatics tool called pat- GPCR, to predict the 3D structures of transmembrane helices of G-protein-coupled receptors. patGPCR, a parallelized multi template approach, extends a bundle-packing related energy function to RosettaMem energy, which improves the TM RMSD (root mean square deviation of the transmem- brane helices) of the predicted models. N. Deng et al. contribute another platform, called crcTRP, for colorectal cancer. This server provides the translational research of colorectal cancer by providing various types of biomedical information, including clinical data, epidemiology data, individual omics data, and public omics data.
3.0 Clinical Research Informatics
Within the 2018 International Medical Informatics Association (IMIA) Yearbook, the goal of the Clinical Research Informatics section is to provide an overview of research trends from 2017 publications that demon- strate excellent research about multifaceted aspects of medical informatics supporting clinical trials and observational studies. Clin- ical Research Informatics (CRI) continues to be developed and the CRI community has especially to address the important challenges of sharing health data with the best balance
“Between Access and Privacy” – this year’s special theme for the IMIA Yearbook. New methods, tools, and CRI systems have been developed in order to collect, integrate and mine healthcare data for better care. Research in the CRI field continues to accelerate and to mature, leading to tools and platforms deployed at national or international scales with encouraging results. Beyond securing these new platforms for exploiting large-scale health data, another major challenge is the limitation of biases related to the use of “real-world” data. Controlling these biases is a prerequisite for the development of learning health systems.
The field of clinical informatics has expand- ed substantially in the six decades since its inception. When clinical informatics was first introduced, simple demonstrations that various information technology enabled processes such as clinical documentation, order entry, medical diagnosis, or therapy planning were possible were sufficient to gain attention, funding, and even limited clinical use. As these techniques and technologies have become more widely available, the need for high-quality evaluations to provide scientific evidence has increased. With the recent emphasis on comparative effectiveness research. the need to develop new methods for and conduct rigorous evaluations of all aspects of health information technology (HIT) [2] will continue to grow. We divided the evidence into three primary themes: 1) clinical informatics systems and interventions for providers, 2) consumer health informatics systems, and 3) methods and governance for clinical informatics. The first theme, clinical infor- matics systems and interventions, includes two main topics: a) electronic health records (EHRs), computerized provider order en- try (CPOE), and clinical decision support (CDS); and b) health information exchange (HIE). The second theme, consumer health informatics systems includes two main topics: a) personal health records (PHRs); and b) web-based and mobile HIT. The third theme, methods and governance for clinical informatics, includes three main topics: a) EHR usability; b) data mining, text mining, and natural language processing (NLP); and c) privacy and security. Over the last several years, considerable progress has been made in demonstrating that various clinical informatics methodol- ogies and applications improve the structure, process, or outcome of various facets of the healthcare system. Over the coming years, much more will be expected from the field. As we move past the “early adopters” in Rogers’ diffusion of innovations’ curve [132] through the “early majority” and into the “late majority,” there will be a crucial need for methodologies and applications that have been rigorously demonstrated to work in multiple settings with different types of patients.
5.0 Public Health Informatics
Public health informatics (PHI) is defined as the systematic application of information, computer science and technology in areas of public health, including surveillance, prevention, preparedness, and health promotion. The main applications of PHI are 1. promoting the health of the whole population, which will ultimately promote the health of individuals and preventing diseases and injuries by changing the conditions that increases the risk of the population. Basically, PHI is using informatics in public health data collection, analysis and actions. Emphasis on disease prevention in the population, realizing its objectives using a large variety of interventions, and work within governmental settings are aspects that make PHI different from other fields of informatics. The scope of PHI includes the conceptualization, design, development, deployment, refinement, maintenance, and evaluation of communication, surveillance, and information systems relevant to public health. PHI could be considered one of the most useful systems in addressing disease surveillance, epidemics, natural disasters and bioterrorism. The use of computerized global surveillance and data collection systems, such as health information exchange (HIE) and health information organization (HIO), could assist in population-level monitoring. This could help to avert the negative impact of a widespread global epidemic. Since its early operation, electronic reporting systems have played an unparalleled role in discovering and con- taining the spread of diseases in a timely fashion while pro- tecting lives and improving the health of entire populations by reducing the financial and human impact of diseases on the society as a whole. Several applications and initiatives are currently available to meet the growing needs for faster and accurate data collection methods. For example, the Global Outbreak Alert and Response Network of WHO relies on web-based sources for the purpose of daily surveillance. Mobile apps and social media networks are widely used by the public and can be used as supporting tools added to the PHIN of disease surveillance.
7.0 Conclusion
Applications of medical informatics generally can be aggregated into many aspects, such as an introduction that tells us, why does this medical informatics is important in order to keep data about the patient, the security, how does the data linkage with one another. We also have translational bioinformatics, clinical research informatics, clinical informatics, consumer health informatics and lastly, public health informatics. Medical informatics is the intersection of information science, computer science, and health care. This field deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval and use of information in health and biomedicine. These benefits include better access to health information and health services, improved patient care and safety, greater coordination of care, and more empowered patients.
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