Chapter 17: Health Informatics Skills and Professional Training

17.1 Required skills for healthcare professionals

17.1.1 How health professionals perceive health informatics today

There still is a lack of understanding of what health informatics is among health professionals Health professionals don’t know what they need to know with regard to health informatics Misconceptions exist: health informatics experts are “guys who fix computers in hospitals”… Make your own survey asking your friends. 17.1.2 Understanding the human factor and dealing with changes in the everyday practice

Clinicians feel that IT is challenging their “authority” and their decision autonomy They seem to prefer their own judgment to provide the “right treatment” Healthcare professionals feel they HAVE to follow standards and rules set by the new technologies Transition from the old systems to the new e-health technologies may be an additional barrier:

during this transition, both paper & e-systems exist, increasing time commitment. This may be frustrating for clinical staff

Interpersonal barriers: technology cannot solve these. There is therefore the need to develop a collaborative environment

17.1.3 Change of culture within the workforce and the role of management

Managers must encourage decisions about what information is relevant to improve quality of care. Health Professionals should be Active and Pro-active Move past initial reservations that they feel by proper education and support Overcome initial embarrassment of technical skill illiteracy-especially older health professionals Enhance the spirit that they WILL become proficient-they can do it Users must adapt and respond quickly to changes and that could lead to failure of the project. ------------------------- IT systems make administrators more efficient: they are expected to use of multiple information

sources simultaneously Organizations should allow administrators make sure that information is being used effectively “From short to long-term goals”- e.g. quality indicators, patient safety monitoring Evidence Base: Classifications, Terminologies are well integrated into practice now

IT systems should support evidence-based practice and convince healthcare professionals of its value. Health professionals need to know how to use these terminologies There are specific rules on how to input information into health information systems The input is usually performed in the background but there are cases that there should exist at least

a basic knowledge of the terminology and the classification systems

17.1.4 How training for health professionals is delivered

Seminars prior to the implementation of the Health Information Systems Online training and self evaluation Simulations using the real software with non-real patients, to tackle potential problems Support during practice by IT experts is an important prerequisite for success Health Informatics Literacy should not always considered as a fact Users can move a mouse to operate a computer (?) File structure (files, folders, sub-folders etc) Word processing Simple excel worksheets Storing files and retrieving them

17.1.5 An interesting Questionnaire Survey

[Garde S, Harrison D, Huque M, Hovenga EJ. Building health informatics skills for health professionals: results from the Australian Health Informatics Skill Needs Survey. Aust Health Rev. 2006 Feb;30(1):34-45.]

The researchers of this study explored the health professionals’ perceived degree of competency required for skills in five skill categories based on the International Medical Informatics Association recommendations on education.

Based on the internet based survey the highest ranked competencies were the following:

Expressing complex knowledge in plain English Effective communication between health & IT

professionals Social competency

Health data, information and knowledge management

Evidence-based practice Change management Electronic health records Clinical guidelines Quality and safety management Project management

Health care organization and administration

Risk management Ethics

Management of health information systems Outcome measurement/ practice evaluation Health information systems (general

characteristics, architecture)

Physiology Health concept representation Clinical disciplines (internal medicine, surgery)

Anatomy Diagnostic and therapeutic strategies Business management Business analysis/ workflow analysis

17.2 Health Informatics Experts: Skills and Jobs Is Health Informatics a profession?

Health Informatics is not yet a mature profession UK Council of Health Informatics Professions suggested eight key areas - information management,

knowledge management, project management, ICT, education and research, clinical informatics, health records, health informatics service management

These constituencies accommodate professionals in and for the NHS, in academia and commercial service and solution providers.

Since the 1970s the most prominent international coordinating body has been the International Medical Informatics Association (IMIA)

Different disciplines involved into the education of health informatics experts.

Source: William Hersh. A stimulus to define informatics and health information technology. BMC Med Inform

Decis Mak. 2009; 9: 24.

Competencies required in Biomedical and Health Informatics

Source: William Hersh. A stimulus to define informatics and health information technology. BMC Med Inform

Decis Mak. 2009; 9: 24.

17.2.1 Professions required to support e-health organizations

IT professionals Install, maintain, and optimize hardware and software. Clinical informaticians Intersection of health care and IT to assure successful adoption of health information technology Optimize the use of information though leadership Bridge between IT and clinical personnel

[Each of America's 6000 hospitals and other health care settings will require their expertise to make the best clinical use of HIT. Est. of 10,000 to 13,000 needed]

Health Information Management professionals Bring knowledge and skills on increasingly electronic medical records, especially in areas of

documentation, coding, compliance issues

[170,000 HIM professionals in the field, expected to grow to >200,000 by 2016]

Other professionals in health care IT Health information management (HIM) professionals IT professionals, often with computer science or management information systems backgrounds Health science librarians Clinicians into IT roles with or without formal training

17.3 What we need to teach future experts in health informatics 17.3.1 Curriculum 10x10 by American Medical Informatics Association

Biomedical and health informatics to those who will work in healthcare and information technology [Gets its name from a 2005 goal of educating 10,000 healthcare & related professionals in

biomedical & health informatics by 2010] Introductory training to build the workforce that will enable information technology to improve the

quality, safety, and cost-effectiveness of healthcare and public health. 17.3.2 Education and training in the United States Since a highly multi-disciplinary field, no standard curriculum or accreditation Listing of programs on Web site of American Medical Informatics Association (www.amia.org) Education historically focused on academics but is evolving to meet needs of practitioners & users New emerging education needs… For nurses (Nursing Informatics) For doctors (Medical Informatics) For decision and policy makers For public health professionals (Public Health Informatics)

17.3.3 Focus disciplines for the education of future experts on health informatics Overview of Field & Problems Motivating It What is biomedical and health informatics. Role in healthcare, public health, biomedical research Major problems in healthcare motivating use of medical informatics Understand roles of individuals in the HIT workforce. Major sources of information for medical informatics in scientific literature and World Wide Web Biomedical Computing Inform optimal selection of hardware, software, and network connections for a given health or

biomedical setting Major aspects of software engineering as they relate to biomedical and health informatics Specify use cases for health informatics functionalities Electronic and Personal Health Records (EHR, PHR) Major categories of clinical data, content and structure Essential functions of the electronic health record (EHR) Major barriers to EHR use Nursing informatics

Standards and Interoperability Importance of standards and interoperability for health and biomedical data Message exchange standards and the type of data they exchange Terminology systems used in biomedicine, content, limitations Privacy, Confidentiality, and Security Privacy, confidentiality, and security HIPAA and other privacy and security issues in healthcare

Meaningful Use of the EHR Patient safety and causes of medical error Healthcare quality and how the EHR enables them Clinical decision support: use and limitations in clinical practice Computerized provider order entry and challenges to its use Information exchange Evidence-Based Medicine (EBM) Evidence based medicine & comparative effectiveness research Clinical Questions and evidence based answers Key statistics of EBM (ie relative and absolute risk) How to implement EBM in clinical settings Information Retrieval and Digital Libraries Biomedical knowledge resources (e.g. books, journals, electronic databases)

Structure & content of digital libraries and major issues facing them Describe major approaches to indexing knowledge-based content Advanced searching techniques to major biomedical knowledge resources Imaging Informatics Management of images in clinical settings, incl. use of PACS Different modalities of imaging, capture and use in digital form Telemedicine Types of telemedicine and their uses Efficacy of telemedicine as shown in clinical studies Different approaches to patient-provider communications

Translational Bioinformatics Aspects of bioinformatics and distinguish its work from other areas of health informatics. Role of genetics and genomics in biology and medicine Major techniques of bioinformatics, emerging approaches in gene expression, gene variation, and

their association with the phenotype Concept of personalized medicine and how it is enabled by biomedical and health informatics Major bioinformatics data resources and demonstration of their use 17.4 Need for interprofessional education FOR interprofessional collaboration

Interprofessional Learning (IPL) is delivered in either a classroom and/or practice setting. The process of IPL is predominantly experiential and incorporates shared learning, cooperative learning, action learning and e-based learning.

Why Interprofessional Education?

IPE for health professionals is required in an e-health environment To modify negative attitudes and perceptions To enhance trust & communication between professions To reinforce collaborative competence To cope with problems exceeding the capacity of any profession To enhance job satisfaction and ease stress

To “simplify things” in response to technological advance To integrate specialist and holistic care

WHO “Learning Together to Work Together”

Chapter 18: User Modeling

18.1 Introduction and Definitions

User modeling is a subdivision of human-computer interaction Main goal of user modeling is customization and adaptation of systems to the user's specific needs. The system needs to "say the 'right' thing at the 'right' time in the 'right' way". To do so it needs an internal representation of the user. Another common purpose is modeling specific kinds of users, ( i.e. their skills, knowledge etc for use

in automatic software apps) A user model represents a collection of personal data associated with a specific user. It is the basis for adaptive changes to the system's behavior. Which data is in the model depends on the purpose of the application.

[personal information, their interests, their skills etc or data about their behavior and their interactions with the system]

Different design patterns for user models. Often a mixture of them is used How systems collect data from users Asking for specific facts while (first) interacting with the system Finding out about users' preferences by observing & interpreting their interactions with the system [Machine learning algorithms may be used to accomplish this task] A hybrid approach which asks for explicit feedback

User modeling and healthcare services

Need for personalized care Constant changes in individual needs over time Adaptation as part of the treatment process! Changes in patient status happen in short and long term Systems interact with patients for monitoring, assisted living, rehabilitation These systems should be tailored to the patient’s needs

18.2 Types of User Models

Static user models: the most basic kinds of user models. Once the main data is gathered they are normally not changed again. Dynamic user models: allow a more up to date representation of users. Changes in their interests, their learning progress or interactions with the system are noticed and influence the user models. The models can thus be updated and take the current needs and goals of the users into account. Stereotype based user models: based on demographic statistics. Based on the gathered information users are classified into common stereotypes. The system then adapts to this stereotype. Highly adaptive user models: represent one particular user and therefore allow a very high adaptivity of the system. This kind of model needs to gather a lot of information first. and alters the user model by adaptive learning

18.3 System adaptation

The system adaptations may concern every aspect of the system's behavior and depend on the system's purpose.

Information and functions can be presented according to the user's interests, knowledge etc by displaying only relevant features, hiding irrelevant information, making proposals etc

The user can manually change the system's appearance, behavior or functionality by actively selecting the corresponding options.

Afterwards the system will stick to these choices. In an adaptive system a dynamic adaption to the user is automatically performed by the system

itself, based on the built user model.

18.4 User Modeling Methods Rule Based Filtering

A set of IF... THEN... rules is established that covers the knowledge base of the system The IF-conditions can check for specific user-information and if they match the THEN-branch is

performed which is responsible for the adaptive changes.

e.g In the case of a TBI patient interacting with a rehabilitation instrument with cognitive games D-O Y-O-U W-A-N-T T-O P-L-A-Y M-O-R-E ? If the patient says “yes” to the question, the voice recognition module understands the reply and the system adopts and starts a new session Collaborative Filtering

Information about a user is compared to that of other users of the same systems. If these characteristics match those of another, the system presumes that the patient is likely to have

similar characteristics in areas where the model of the current user is lacking data. Based on these assumptions the system performs adaptive changes.

System: Should I play Mozart music to relax the patient? But I do not know his music preferences! Let me check the database……after a few milliseconds… Other patients of the same gender and age from the same city and with the same occupation, like Mozart! Let’s play some then!

How Collaborative Filtering Works

18.5 User Modeling Applications

18.5.1 Adaptive hypermedia The displayed content is chosen based on users' specific characteristics (goals, interests, knowledge, and abilities). Adaptive hypermedia aims to only present relevant information to the user. Adaptive educational hypermedia: main focus lies on education, displaying content & hyperlinks corresponding to the user's knowledge on the field of study. 18.5.2 Intelligent tutoring systems Stand-alone systems which help students in a specific field of study They build up a user model where they store information about abilities, knowledge and user needs The system can now adapt to this user by presenting appropriate exercises and examples and

offering hints and help where the user is most likely to need them. 18.5.3 User-Simulation Since user modeling allows the system to hold an internal representation of a specific user, different

types of users can be simulated by artificially modeling them. Common types are "experts" or "novices" on the scope of the system or the usage of the system. 18.5.4 Recommender system Present a selection of items to the user which best fit their needs. This selection can be based on items the user has bookmarked, rated, bought, recently viewed, etc. Recommender systems are often used in e-commerce but may also cover areas like social networks,

websites, news, etc. 18.5.5 Expert Systems Expert systems are computer systems that emulate the decision-making ability of human experts to help the user solving a problem in a specific area. The system asks questions one by one to identify the current problem and find a solution. The system can assume that experienced users can answer more complex questions than someone new to the topic. Therefore, it can adjust the used vocabulary and the type of question which are presented to the user, thus reducing the steps needed to find a solution. Expert systems in healthcare are very common To help health professionals make decisions Path-ways for decision making