Cerner Application Information System in Specialized Dental Centers Evaluation Proposal

The Rationale for Selecting the Topic

Introduction to the Topic

Technologies have entered practically every sphere of humans’ personal and professional lives, thus making a variety of functions simpler and more accessible. In healthcare settings, the use of technological advancements has led to a great number of improvements. One of such enhancements is the possibility to simplify the process of keeping and using patient records. Cerner is the information system used for making appointments and recording patient data within healthcare settings (“We understand your challenges,” n.d.). The reason for analyzing Cerner as an information system is to establish the benefits and limitations of using this technology in the dental care environment.

Description of the Healthcare Setting

The healthcare setting is a specialized RAK dental center offering a whole specter of dental services to its clients. Apart from usual services, the center performs dental surgeries and employs pediatric dentists who have been trained specifically for working with young patients. The clinic is open from 7.30 a.m. till 9.30 p.m., which allows serving the needs of all customers. Nineteen doctors and twenty-three dental assistants are employed at the center.

Objectives

The major objective of the project is to evaluate the information system used by the staff and to assess the possibility of employing Cerner at the dental center. To pursue this goal, the survey of employees will be conducted, which will show their attitude toward the application of Cerner, as well as their suggestions on the enhancement of this system. The researcher will also conduct interviews and perform self-evaluation measures.

Significance

The importance of the project is justified by the fact that the system in this dental center has not been evaluated before. Meanwhile, it is crucial to investigate the positive and negative sides of the information system since it has a great effect on employees’ work and, consequently, on patients’ satisfaction. Upon performing a detailed analysis of the system, it will be possible to mitigate its drawbacks.

Literature Review

Electronic Health Records: Cerner

Scholars have paid due attention to the investigation of Cerner, as well as other electronic health systems, in various healthcare settings. The majority of research articles are focused on the analysis of Cerner in different medical environments. In their study of Cerner use in a large teaching hospital, Andersen et al. (2009) have found that the system comprises a variety of features, such as the possibility to order tests, view results, and manage medications electronically.

However, the authors note that the ward space peculiarities and the devices available to doctors and nurses can limit the affordability of Cerner’s benefits (Andersen et al., 2009). Buchanan and Alexiou (2011), who have studied the use of Cerner ProVision Document Imaging, report that healthcare employees may experience difficulty bar-coding patient data and other documents, as well as managing the turnaround times of health information systems and securing quality at the same time.

Burgess et al. (2009) emphasize the effectiveness of Cerner use in a hematology department. Scholars note that the system allows making crucial medical decisions based on the integration of such data as patients’ demographic information, previous results, and the analysis of errors and numeric results (Burgess et al., 2009). Jones et al. (2009) also acknowledge the benefits of Cerner, but the object of their investigation is a heart failure department.

Researchers note that compared to paper records, Cerner is much more convenient and time-saving (Jones et al., 2009). Specifically, Cerner enables healthcare specialists to optimize medical records and create post-admission patient care plans. The effectivity of Cerner for discharge summaries has also been discussed in Kazmi’s (2008) study. The scholar reports that the use of Cerner promotes a simplified collection and share of patient data before, during, and after hospital admission. Kazmi (2008) notes that an evident benefit of the system is the possibility to add new information to patients’ records at any time.

A group of articles is concentrated on analyzing the pros and cons of Cerner features. Lechleitner et al. (2003) and Pogue et al. (2014) emphasize the positive functions available to the facilities using Cerner. According to Lechleitner et al. (2003), Cerner is a user-friendly system enabling personnel to schedule important events and document data easily. Meanwhile, Pogue et al. (2014) remark that despite opportunities offered by Cerner, the system also requires powerful technology resources. Since some health facilities cannot afford these, the use of Cerner will not be equally effective in different settings.

Lechleitner et al. (2003) also point out some weaknesses of Cerner, so scholars suggest making some adaptations to the system depending on the clinics’ specifications. Payne et al. (2005) have investigated the Online Record of Clinical Activity (ORCA), based on Cerner. The authors note the following features of electronic records: managing, editing, forwarding, and signing of documents. It is emphasized that patients’ health records are easier to complete and analyze when they are kept in the electronic form.

Another set of scholarly studies concentrates on various applications of electronic health records in healthcare institutions. Del Becarro et al. (2010) have found that the use of Cerner systems enables healthcare specialists to reduce the rate of medication orders prompting a dose change alert to a great extent. Kudler and Pantanowitz (2010), who have analyzed laboratory data tools incorporated in Cerner medical records, report that there are numerous advantages of flexible electronic data, such as better analysis support, improved decision making, and simplified charting.

Integrated data capture is the focus of research in Laird-Maddox et al.’s (2014) study. Scholars note that the use of Cerner not only improves the process of keeping electronic records but also increases the possibilities for medical-related research. Specifically, instead of manual data input and analysis, healthcare specialists are able to perform these actions electronically, which boosts their efficiency and allows more time for conducting research.

Finally, several studies have aimed at comparing the effectiveness of Cerner to that of other electronic medical systems. Parkash et al. (2014), who have evaluated Cerner and EpicCare, conclude that both systems have considerable drawbacks. The authors note that it is sometimes difficult to track the patients’ referral chain to various specialists. Neither Cerner nor EpicCare offers an indication of amended alerts on the screen.

However, Cerner at least shows a generic label ‘modified’ if the changes are viewed in the chronologic sequence (Parkash et al., 2014). The article by Sittig et al. (2015) compares eight systems, including Glassomics, eClinicalWorks, Eric Hyperspace, Meditech, and others. Findings indicate that Cerner has some advantages over other systems under analysis, but none of the eight electronic health record options is maximally convenient for users. Westbrook et al. (2012), who compared Cerner to iSoft MedChart, have found that the use of Cerner leads to a considerable decrease in clinical error rates associated with e-prescribing systems. Due to the lack of control opportunity in the hospital where iSoft MedChart was applied, Cerner may be viewed as a more successful option.

Limitations of Cerner in Dental Clinics

Cerner offers a variety of options aimed at simplifying medical employees’ work and making it more efficient. The main components of the system include a convenient clinical portal, a document registry for data maintenance, and a document repository for information storage (“Cerner network,” n.d.). However, the analysis of healthcare workers’ attitudes toward the use of Cerner, as well as other electronic records, indicates that many of them are not willing or ready to utilize these technologies at work. This issue especially concerns dental centers and clinics, and the review of the following sources will show the causes of such reactions.

Edsall and Adler (2015) have found that generally, physicians are not satisfied with electronic health records. However, even despite being dissatisfied, they do not find it easy to find more effective systems to which they would be willing to switch. Among those favored at present, Cerner occupies the sixth position out of ten (Edsall & Adler, 2015). Alnuaimi et al. (2011) report that there are three main outcomes of implementing electronic records acknowledged by healthcare professionals, including dental specialists: increased resistance, lower job satisfaction, and higher job-related stress. Additionally, Alnuaimi et al. (2011) and Spallek et al. (2014) emphasize the high cost of educating dental healthcare specialists about the use of electronic records.

Scholars note that the incorporation of dental diagnostic terminology into electronic records is possible but quite complicated (White et al., 2011). Hence, currently, the adoption rate of Cerner among dental practitioners is low, the main reasons for such statistics being the cost of software, difficulty using, and refusal to learn demonstrated by older dentists (Acharya et al., 2017). Kalenderian et al. (2016) and Walji et al. (2014) acknowledge the lack of an effective electronic dental database. However, Cerner does not seem to be the most suitable and convenient system to meet these needs.

References

Acharya, A., Schoeder, D., Schwei, K., & Chyou, P.-H. (2017). Update on electronic dental record and clinical computing adoption among dental practices in the United States. Clinical Medicine & Research, 15(3-4), 59-74. Web.

Alnuaimi, O. A., Cronan, T. P., Douglas, D. E., & Limayem, M. (2011). Healthcare professionals’ reactions to health enterprise system implementations: A theory of cynicism perspective. Proceedings of the 44^th Hawaii International Conference on System Sciences. Web.

Andersen, P., Lindgaard, A.-M., Prgomet, M., Creswick, N., & Westbrook, J. I. (2009). Mobile and fixed computer use by doctors and nurses on hospital wards: Multi-method study on the relationships between clinician role, clinical task, and device choice. Journal of Medical Internet Research, 11(3), e32.

Buchanan, R., & Alexiou, C. (2011). Medical record scanning project, Alfred Health, Melbourne: Implementation of Cerner ProVision Document Imaging. HIM-Interchange, 1(1), 7-9.

Burgess, P., Robin, H., Langshaw, M., Kershaw, G., Pathiraja, R., Yuen, S., Coad, C., Xiros, N., Mansy, G., Coleman, R., Brown, R., Gibson, J., Holman, R., Hubbard, J., Wick, V., Lammers, M., Johnson, R., Huffman, K., Bell, J., … Joshua, D. (2009). Rule based processing of the CD4000, CD3200 and CD Sapphire analyser output using the Cerner Discern Expert Module. International Journal of Laboratory Hematology, 31, 603-614. Web.

Cerner network. (n.d.). Secure exchange of medical information: Clinical exchange platform. Web.

Del Beccaro, M. A., Villanueva, R., Knudson, K. M., Harvey, E. M., Langle, J. M., & Paul, W. (2010). Decision support alerts for medication ordering in a Computerized Provider Order Entry (CPOE) system. Applied Clinical Informatics, 1(3), 346–362. Web.

Edsall, R., & Adler, K. G. (2015). EHR switch survey: Responses from 305 family physicians. Family Practice Management, 22(1), 13-18.

Jones, N., Venturella, A., Waguespack, C., Hoppe, C., Moldthan, S., & Arnold, S. (2009). Innovative, interprofessional heart failure clinical pathway in Cerner Electronic Medical Record. Heart & Lung: The Journal of Cardiopulmonary and Acute Care, 44(6), 556.

Kalenderian, E., Halamka, J. D., & Spallek, H. (2016). An EHR with teeth. Applied Clinical Informatics, 7(2), 425-429.

Kazmi, S. M. B. (2008). Quality of electronic discharge summaries at Newham University Hospital: An audit. British Journal of Medical Practitioners, 1(1), 30-32.

Kudler, N., & Pantanowitz, L. (2010). Overview of laboratory data tools available in a single electronic medical record. Journal of Pathology Informatics, 1(1), 3. Web.

Laird-Maddox, M., Mitchell, S. B., & Hoffman, M. (2014). Integrating research data capture into the electronic health record workflow: Real-world experience to advance innovation. Perspectives in Health Information Management, 1(11), 1e.

Lechleitner, G., Pfeiffer, K.-P., Wilhelmy, I., & Ball, M. (2003). Cerner Millennium™: The Innsbruck experience. Methods of Information in Medicine, 42(1), 08-15. Web.

Parkash, V., Domfeh, A., Cohen, P., Fischbach, N., Pronovost, M., Haines, G. K., & Gershkovich, P. (2014). Are amended surgical pathology reports getting to the correct responsible care provider? American Journal of Clinical Pathology, 142(1), 58-63. Web.

Payne, T. H., Kalus, R., & Zehner, J. (2005). Evolution and use of a note classification scheme in an electronic medical record. AMIA Annual Symposium Proceedings Archive, 2005, 599-603. Web.

Pogue, J. M., Potoski, B. A., Postelnick, M., Mynatt, R. P., Trupiano, D. P., Eschenauer, G. A., & Kaye, K. S. (2014). Bringing the “power” to Cerner’s PowerChart for antimicrobial stewardship. Clinical Infectious Diseases, 59(3), 416-424. Web.

Sittig, D. F., Murphy, D. R., Smith, M. W., Russo, E., Wright, A., & Singh, H. (2015). Graphical display of diagnostic test results in electronic health records: A comparison of 8 systems. Journal of the American Medical Informatics Association, 22(4), 900-904. Web.

Spallek, H., Johnson, L., Kerr, J., & Rankin, D. (2014). Costs of health IT: Beginning to understand the financial impact of a dental school EHR. Journal of Dental Education, 78(11), 1542-1551.

Walji, M. F., Kalenderian, E., Stark, P. C., White, J. M., Kookal, K. K., Phan, D., Tran, D., Bernstam, E. V., & Ramoni, R. (2014). BigMouth: A multi-institutional dental data repository. Journal of the American Medical Informatics Association, 21(6), 1136-1140. Web.

We understand your challenges. We can help. (n.d.). Web.

Westbrook, J. I., Reckmann, M., Li, L., Runciman, W. B., Burke, R., Lo, C., Baysari, M. T., Braithwaite, J., & Day, R. O. (2012). Effects of two commercial electronic prescribing systems on prescribing error rates in hospital in-patients: A before and after study. PLoS Medicine, 9(1), e1001164. Web.

White, J. M., Kalenderian, E., Stark, P. C., Ramoni, R. L., Vaderhobli, R., & Walju, M. F. (2011). Evaluating a dental diagnostic terminology in an electronic health record. Journal of Dental Education, 75(5), 605-615.