Scrutinizing the Literature of EMR

 As I scrutinize Dimitropoulos and Rizk (2009) for possible inclusion in a literature review for my research, I find it both promising and troubling. The article appears to be pertinent to my research question of how various laws and practices might adversely affect shared access of electronic health records; however, it is important to understand if this article is a documentation of primary research or a review of existing research, and as I describe below, this is unclear. This lack of clarity obscures other facets of the article that important to a researcher. These are also described below.

Initially, the work of Dimitropoulos and Rizk appears to be pertinent to my research based on the title and the publication in which it appears. Health Affairs is a respected journal within the realm of public health research, practice, and instruction, and it is ranked seventh of all health policy and service journals by Journal Ranking ( Publication within Health Affairs does not degrade the reputation of the authors and serves only to promote their work to their peers. As my research is within the realm of public health, Health Affairs is an obvious avenue to pursue for relevant work, and as this article by Dimitropoulos and Rizk appears to reflect a specific focus on the relationship between privacy laws and the ability, or lack thereof, to share health information, it appears to have relevance.

According to the abstract, Dimitropoulos and Rizk (2009) examine how variations is state (and, territorial) privacy laws might inhibit sharing health information via an central exchange, or repository. Though it would seem plausible for Dimitropoulos and Rizk to conduct their own research, the abstract seems to imply that they are merely reporting on the findings of a committee charged with examining such irregularities in privacy laws amongst the states and territories, presumably, of Canada. After reading the report, though, I find a disconnect between the abstract and the article. In the abstract, it appears more as if the authors are detached reporters, but within the body of the article, it seems as though they appear to take ownership of the primary research. This is confusing as it was plainly stated that the research was conducted by a large consortium of state officials: “the project initially engaged organizations in thirty-four … and later … forty-two jurisdictions. This collaborative work is commonly referred to as the Health Information Security and Privacy Collaboration (HISPC)” (p. 429).

This report is confusing to read as the perspective shifts frequently between first- and third-person. Additionally, the authors describe opinions formed and emotions felt during the primary research (opinions and emotions that only the primary researchers could know), yet it is unclear if these were transmitted through other writing or if the authors formed and felt these themselves. It is unclear whether the authors, Dimitropoulos and Rizk (2009), were participating researchers or merely reporters.

Both authors are noted to work for RTI International’s Survey Research Division, yet this corporation is not credited with any of the original research (Dimitropoulos & Rizk, 2009). I would have to conduct further research into the authors, their employer, and the project, itself, in order to make a final determination of the credibility of this article. This research would, hopefully, give the authors’ words better context, also. Complicating this is the absence of clearly delineated references, although a few appear within the Notes section that appear to be worth investigating.

Dimitropoulos and Rizk (2009) describe an effort to create a cohesive environment that will enhance the ability to share health information throughout a number of jurisdictions. As such, there is no scientific inquiry and it follows that adherence to the scientific method would be inappropriate. Again, however, it is unclear if this research is original or not.

In closing, it appears that Dimitropoulos and Rizk (2009) are credible in their writing; however, as each article must be able to stand on its own, and the article is lacking in form and perspective, I question the origination, application, and utility of this article, at least as it pertains to my original research question. Privacy in computing has been a major concern in the past two decades (Johnson, 2004). I feel that I could find more pertinent literature by expanding my search beyond this article.


Dimitropoulos, L. & Rizk, S. (2009).A state-based approach to privacy and security for interoperable health information exchange.Health Affairs, 28(2), 428-434. doi:10.1377/hlthaff.28.2.428

Johnson, D. G. (2004). Computer ethics. In L. Floridi (Ed.), The Blackwell guide to the philosophy of computing and information (pp. 65-75). Malden, MA: Blackwell.

Using Intelligence in ePCR Database Design

The intelligence of a database design begins with the intelligent approach in which the developer focuses on the particular need the database is to fulfill. It is especially important to constrain, or specialize, a database used in health care, else the database can quickly grow beyond the bounds of efficiency. Efficiency can be found directly from table design, and it can be further achieved with business rules and logic. Designing a database for storing patients’ medical records also has some risk of increasing the likelihood of medical errors and statistical incongruities if done improperly; therefore, a qualified database administrator should be consulted (Campbell, 2004; McGlynn, Damberg, Kerr, & Brook, 1998). However, a preliminary needs assessment can be accomplished by asking a few simple questions: Who? What? Where? Why?

Who needs to use the database? For whom is the data useful? By identifying the scope, or domain, of each database user, the developer can gain a sense of which data points are important (McGlynn et al., 1998; Thede, 2002). For instance, in health care, a purely diagnostic database should efficiently offer comparative differential diagnoses to aid a physician in caring for patients; however, a database of this type will not offer much to the administrative arm of the practice. By understanding the relationship between physician diagnosis and billing, relational techniques can serve to ensure greater accuracy in billing procedures.

What data needs to be stored and retrieved? By listing the specific data to be stored, the developer has an opportunity to optimize the storage methods by creating an efficient and normal relational table foundation (Kent, 1983; Sen, 2009). A patient care reporting database, for instance, must be able to store patient identifying information, or demographics. Depending on the specific needs of the practice, demographic data can usually be stored in a single table. Other relational tables could be used to store references between the patient demographic record and pertinent medical information, thereby minimizing duplication (Thede, 2002).

From where does the data need to be accessed? Does this database require authentication for use on a local area network or a complex security policy for wide area network access (Campbell, 2004; McGlynn et al., 1998)? More importantly, however, is portability of the data. If the data is going to be replicated in a large composite database, the data needs to meet the specifications of the repository. This is often achieved by the publication of a template, or a clear set of directives on how data is to be formatted before transmitting data to the repository. An example of this is the Medicare electronic records requirements set forth in the Health Insurance Portability and Accountability Act (HIPAA) of 1996. By accounting for common templates in the design phase, the developer can avoid having to parse data prior to transmitting the data over the network.

Why are we storing the data? Today, it is very common to store data if merely for purposes of recording an interaction, such as a patient contact. However, it is important to understand how the data will be used in the future. Will the data need to be immediately accessible, such as in emergency or critical care areas, or could the data be compiled and batch processed during times of off-peak network load, such as in billing or logistics. Could paper reporting fulfill the immediate need better? If so, should the data on the paper report be entered in a database later? Regarding transcription, it is important to be knowledgeable about the available technology for creating scanned images, portable electronic documents, and the use of optical character recognition in order to properly prepare for the storage of each.

By answering the who, what, where, and why of the database needs assessment, we ultimately answer the question of how to design and implement the database. As an example, in order to design an ambulance run form, we must take into consideration demographics, the history of present illness (or, the reason for the ambulance request), past and pertinent medical history, including, but not limited to: medications, past medical problems and surgeries, and allergies to medications and environment. It is also important to store the assessment, care, and outcome, as well as the disposition of the incident and the destination facility. Additionally, medical standards, such as diagnostic codes, medications, protocols, and algorithms, could be stored in reference tables for preventing redundancy within the data model (Kent, 1983; McGlynn et al., 1988; Sen, 2009, Thede, 2002). Ambulances are mobile; therefore, network access is an important consideration when designing an electronic ambulance patient care reporting database. For this type of database schema, I would recommend using a small, efficient database locally with a mechanism in place to replicate the data to the larger repository when the network is accessible.

Another challenge in creating a database is learning how not to store information. Information is made of of data, but only data should be stored (Collins, 2009). Programming logic can be used to synthesize data into information and, further, into knowledge. Many database designers mistakenly store information, or even knowledge, quickly inflating the size of the database and decreasing its efficiency and normalcy (Kent, 1983; Sen, 2009).

In conclusion, developing an electronic patient care reporting database for a physician practice has some inherent risk if done poorly; however, a knowledgeable member of the office team can highlight the project requirements by performing the needs analysis.


Campbell, R. J. (2004). Database design: What HIM professionals need to know. Perspectives in Health Information Management, 1(6), 1-15. Retrieved from

Collins, K. (2009). Managing information technology. Exploring Business (pp. 122-130). Retrieved from

Health Insurance Portability and Accountability Act (HIPAA) of 1996, P.L.104-191. (1996).

Kent, W. (1983). A simple guide to five normal forms in relational database theory. Communications of the ACM, 26(2), 120-125. Retrieved from simple5.htm

McGlynn, E. A., Damberg, C. L., Kerr, E. A., & Brook, R. H. (1998). Health information systems: design issues and analytical applications. Retrieved from

Sen, A. (2009, May 7). Facts and fallacies about first normal form. Retrieved from

Thede, L. Q. (2002). Understanding databases. In S. P. Englebardt & R. Nelson, Health care informatics: an interdisciplinary approach (pp. 55-80). St. Louis, MO: Mosby.

Information Theory in Health Informatics

Contemporary information theory has its roots in the development of telephony. During the middle of last century, an engineer at Bell Telephone Laboratories, Dr. Claude E. Shannon, innovated information theory by extending the mathematical observations of Boltzmann, Szilard, von Neumann, and Wiener in the area of physics, quantum mechanics, and particle physics (Weaver, 1949). Dr. Shannon, however, applied the theory to communication technology, introducing entropy to the theory (Nelson, 2002; Weaver, 1949).

Weaver, who worked at the Sloan-Kettering Institute for Cancer Research, adopted Shannon’s technical message transmission observations and adapted them with his understanding of the semantics of a messages meaning (as cited in Nelson, 2002). Shannon and Weaver’s Information and Communication Model details both the components of a message and the requirements of delivery. An example, as it would relate to health care informatics, would be when a nurse charts a patient’s medical history by encoding it via a desktop client application and the same data is viewable by the same nurse at other computer terminals, other nurses, and the treating physician. The data is also stored along the communication pathway for future retrieval and delivery when the patient presented again. Though this example satisfies Shannon, if the intended recipient were blind, the information shown on a computer screen would be meaningless, according to Weaver, and would indicate a limitation to overcome.

Evaluating hospital information systems developed, in part, from the Shannon and Weaver model, Bruce I. Blum (1986) conducted analysis of object (data, information, and knowledge) processing in both hospital and ambulatory care settings. He concluded that system designs should reflect the artificial delineation between these three types of objects and that these systems will benefit practitioners and patients by improving the overall health care process. Blum (1986) called for the “integration of existing systems with medical knowledge and knowledge-based paradigms” (p. 797) in order to have a positive impact on health care delivery in the coming decades.

Information theory is concerned with the adaptability of a message through a particular channel for optimum transmission. In health informatics, as Blum (1986) points out, information theory can be a benefit by improving “[1)] structure — the capacity of the facilities and the capacity and qualification of the personnel and organization, [2)] process — the changes in the volume, cost and appropriateness of activities, [and 3)] outcome — the change in health care status attributed to the object being evaluated” (p. 794). The major challenges, however, would be initial implementation and acceptance (Blum, 1986).


Blum, B. I. (1986). Clinical information systems. The Western Journal of Medicine, 145(6), 791-797. Retrieved from westjmed00160-0055.pdf

Nelson, R. (2002). Major theories supporting health care informatics. In S. P. Englebardt & R. Nelson (Eds.), Health care informatics: An interdisciplinary approach (pp. 3-27). St. Louis, MO: Mosby.

Weaver, W. (1949, September). Recent contributions to the mathematical theory of communication. Retrieved from weaver/weaver.pdf

Implementing an EMR system

Electronic records streamline the flow of many of the components of patient care. EMRs and ePCRs are very useful in lowering costs, simplifying business processes, and increasing patient safety, as well as overall efficiency, if implemented correctly (Smith, 2003).

Currently, I work as a critical care paramedic providing patient care in acute settings, whether prehospital of interfacility. Within this capacity, I also teach classes to other health care providers, including first responders, emergency medical technicians, paramedics, nurses, physicians, and allied health personnel. I am familiar with the concepts of electronic patient care reporting (ePCR) and the importance and utility of electronic medical records (EMR); however, the only means of electronic reporting available in my capacity as a paramedic is poorly developed ePCR software coupled with intermittent network connectivity, so I still choose to utilize paper reporting. My part-time job with a local municipal ambulance provider relies on a widely available third-party ePCR system that seems to work well. I do utilize this ePCR system when working for this provider.

I have also gained experience with information technology and object-oriented programming concepts while developing platform-independent, client-server distributive applications designed for the internet and intranets. I also have experience with Windows and Unix/Linux platforms.


Smith, P. D. (2003). Implementing an EMR system: One clinic’s experience. Family Practice Management, 10(5), 37-42. Retrieved from