Strengths and Weakness of Informatics Review websites of health informatics and other articles in th

Strengths and Weakness of Informatics

Review websites of health informatics and other articles in the USU Academic databases.

Identifying strengths and weaknesses of informatics and the use of EHRs (Electronic Health Records) in healthcare, using a minimum of 5 peer-reviewed references, and no more than 20 references total (the textbook is an excellent reference and may be used as an extra reference, but it is not a peer-reviewed reference). Please contact me if you are unsure of what peer-reviewed literature means.

One of the pages must be a SWOT table (see below or Google). Strengths/Weaknesses/Opportunities/Threats or SWOT analyses are a great way for you to learn about a topic and an extremely useful tool that is actually used in both large and small organizations. Since the majority of you will be working in either private industry or for public entities (military, education, government, etc), this type of analysis will be useful to you in your job. Please list the characteristics of EHRs and informatics in the tables of your SWOT chart which will make it easier to describe in your writing.

Make sure you address the course topics and student learning outcomes covered in the first 4 modules. Your title and reference pages are not counted as part of the 5 – 8. Please follow your APA guidelines.
SWOT Table

Strengths Weaknesses
Opportunities Threats



Discussion #1:

1. Bellinger, G., Casstro, D.& Mills, A. (2004). Data, Information, Knowledge, and Wisdom. Retrieved from
2. U.S. Department of Health and Human Services. Health Information Technology and Quality Improvement. (n.d.). How can you successfully collect and analyze data?. Retrieved from
3. Bess, O., Wallace, M., Torpunari, V., O’Neil, M., Schwend, G., Haughton, J., & … Kolbeck, G. (2012). Looking toward the future: 2012: Industry insiders weigh in on the year ahead in healthcare technology. Health Management Technology, 33(1), 8-15. Retrieved from CINAHL Plus with Full Text.

Discussion #2:

1. Teare, D. (2012). Internetworking Technology Handbook. Indianapolis: Cisco Press, Retrieved from
2. Ratzel, R., & Greenstreet, R. (2012). Toward Higher Precision. Communications Of The ACM, 55(10), 38-47. doi:10.1145/2347736.2347750. Retrieved from Business source Elite.

3. Thomas, B., Jurdak, R., & Atkinson, I. (2012). SPDYing Up the Web. Communications Of The ACM, 55(12), 64-73. Retrieved from Business source Elite.
4. Sreelatha, A. A., VinayaBabu, A. A., Madhukar, K. K., Nagaprasa, S. S., Verghese, D., Mallaiah, V. V., & Pratima, A. A. (2010). Mobile Wireless Enhanced Routing Protocol in Adhoc Networks. International Journal On Computer Science & Engineering, 2398-2401. Retrievede from Academic Search Premier.

Database Models (n.d.). Retrieved from

Discussion #3:
1. Textbook Chapter 7 (pps 211-253):
Shortliffe, E.H., Cimino (eds.) Biomedical Informatics, London: Springer-Verlag, 2014.
2. Szolovits, P. (2003). Nature of Medical Data. MIT, Intro to Medical Informatics: Lecture-2. Retrieved from
3. Blair, J. S. (1999). An Overview of Healthcare Information Standards, IBM Healthcare Solutions. Retrieved from
4. ANSI Standards Activities. Healthcare Information Technology Standards Panel. Retrieved from

Health IT. Gov. Health IT Standards Committee. Retrieved from

5. American National Standards Institute. Healthcare Informatics Standards Board. (n.d.) Inventory if health care information standards: pertaining to the Health Insurance Portability and Accountbility Act (HIPAA) of 1996. (P.L. 104-191). Retrieved from

6.Health Level Seven: Links to Standards Developers. Retrieved from
7. U.S. National Library of Medicine. National Institute of Health. (2003). Unified Medical Language Fact Sheet. Retrieve from

8. Gannot, I.(n.d.) Computers in Medicine. Retrieved from


Discussion #4:

1. Textbook chapters 8 and 12 (Natural Language Processing in Healthcare and Biomedicine, Electornic Health Records Systems)
Shortliffe, E.H., Cimino, J.J. (eds), Biomedical Informatics. London: Springer-Verlag.

2. Johnson, Ronald. (2003). Health Care Technology: A History of Clinical Care Innovation. Health Technology: Special Technology Overview, HCT Project, 1. Retrieved from

3. Mueller, M. L., Ganslandt, T., Frankewitsch, T., Krieglsein, C. F., Senninger, N. & Prokosch, H. U. (2003). Workflow Analysis and Evidence-Base Medicine: Toward Integration of Knowledge-Based Functions in Hospital Information Systems. Department of Medical Informatics and Biomathematics, University of Muenster, Germany. Retrieved from OR

Q&A x 4 weeks:

1. Identify barriers to effective communications in healthcare settings

There are various barriers to effective communications in healthcare settings. Although language can be a huge barrier, the barrier that frustrates me the most at work is “lack of data.” It is imperitive that all nurses chart accurately in the computer; like the old saying, if it hasn’t been charted, it wasn’t done! I realize that the night nurse is tired and wants to go home, but I do not tolerate getting 1/2 of a report because she is in a hurry! There have been far too many times errors are made because the previous nurse did not chart the things she either did or didn’t do, and then followed that up by not mentioning it in shift change. This is proof of how important technology is in the healthcare workplace. These are patient’s lives and they have the right to receive accurate treatment and in order for us to do that, our charting needs to be completed and in the computer 100%. This way, if a nurse forgets to tell me something vital about the patient, I can look it up at anytime during my shift in the patient’s chart on the computer.

2. Explain and Discuss the Open Systems Interconnection Reference Model (OSI) as a framework for communications flow

The OSI or Open System Interconnection, model defines a networking framework to implement protocols in seven layers. Control is passed from one layer to the next, starting at the application layer in one station, and proceeding to the bottom layer, over the channel to the next station and back up the hierarchy.
There’s really nothing to the OSI model. In fact, it’s not even tangible. The OSI model doesn’t do any functions in the networking process, It is a conceptual framework so we can better understand complex interactions that are happening. The OSI model takes the task of internetworking and divides that up into what is referred to as a vertical stack that consists of the following layers:
Physical (Layer 1)
This layer conveys the bit stream – electrical impulse, light or radio signal — through the network at the electrical and mechanical level. It provides the hardware means of sending and receiving data on a carrier, including defining cables, cards and physical aspects. Fast Ethernet, RS232, and ATM are protocols with physical layer components.
Data Link (Layer 2)
At this layer, data packets are encoded and decoded into bits. It furnishes transmission protocol knowledge and management and handles errors in the physical layer, flow control and frame synchronization. The data link layer is divided into two sub layers: The Media Access Control (MAC) layer and the Logical Link Control (LLC) layer. The MAC sub layer controls how a computer on the network gains access to the data and permission to transmit it. The LLC layer controls frame synchronization, flow control and error checking.
– Layer 2 Data Link examples include PPP, FDDI, ATM, IEEE 802.5/ 802.2, IEEE 802.3/802.2, HDLC, Frame Relay
Network (Layer 3)


This layer provides switching and routing technologies, creating logical paths, known as virtual circuits, for transmitting data from node to node. Routing and forwarding are functions of this layer, as well as addressing, internetworking, error handling, congestion control and packet sequencing.
– Layer 3 Network examples include AppleTalk DDP, IP, IPX.
Transport (Layer 4)
This layer provides transparent transfer of data between end systems, or hosts, and is responsible for end-to-end error recovery and flow control. It ensures complete data transfer.
– Layer 4 Transport examples include SPX, TCP, UDP.
Session (Layer 5)
This layer establishes, manages and terminates connections between applications. The session layer sets up, coordinates, and terminates conversations, exchanges, and dialogues between the applications at each end. It deals with session and connection coordination.
– Layer 5 Session examples include NFS, NetBios names, RPC, SQL.
Presentation (Layer 6)
This layer provides independence from differences in data representation (e.g., encryption) by translating from application to network format, and vice versa. The presentation layer works to transform data into the form that the application layer can accept. This layer formats and encrypts data to be sent across a network, providing freedom from compatibility problems. It is sometimes called the syntax layer.
– Layer 6 Presentation examples include encryption, ASCII, EBCDIC, TIFF, GIF, PICT, JPEG, MPEG, MIDI.
Application (Layer 7)
This layer supports application and end-user processes. Communication partners are identified, quality of service is identified, user authentication and privacy are considered, and any constraints on data syntax are identified. Everything at this layer is application-specific. This layer provides application services for file transfers, e-mail, and other network software services. Telnet and FTP are applications that exist entirely in the application level. Tiered application architectures are part of this layer.
– Layer 7 Application examples include WWW browsers, NFS, SNMP, Telnet, HTTP, FTP
Retrieved from: The 7 Layers of the OSI Model –
I believe the importance of the OSI model is for the communication of networks specifically designed for a communication system. It is a system designed for trouble shooting errors in communication, in a way that is logical through step by step through its seven layer model.

3. Discuss the ways the NLP is used to manage biomedical records

Natural language processing has a wide range of potential applications in the biomedical domain. NLP enables a new level of functionality for health care and research-oriented applications that would not be otherwise possible. NLP methods can help manage large volumes of text (e.g. patient reports or journal articles) by extracting relevant information in a timely manner.
The following are important applications of NLP technology in biomedicine:
Information extraction: This locates and structures important information in text, usually without performing a complete analysis.
Information retrieval: this helps users to access documents in very large collections, such as the scientific literature. This is a crucial application in biomedicine, due to the explosion of information available in electronic form. The essential goal of information retrieval is to match a user’s query against the collection and return of most similar documents.
Text generation: this formulates natural language sentences from a given source of information, usually structured data. These techniques can be used to generate text from a structured database, such as summarizing trends and patterns in laboratory data.
User interfaces: this enables humans to communicate more effectively with computer systems. Tools that facilitate data entry are an important application in biomedicine. Data can be captured by keyboard (e.g. a single clinical report such as a discharge summary), or of multiple documents (e.g., multiple journal articles).
Machine translation: this converts text in one language (e.g., English) into another (e.g., Spanish).
Shortliffe, E.H., Cimino (eds.) Biomedical Informatics, London: Springer-Verlag, 2014



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