Belanja Buku di Google Play Jelajahi eBookstore terbesar di dunia dan baca lewat web, tablet, ponsel, atau ereader mulai hari ini. Robert E. Hoyt , Ann K. Health Informatics HI focuses on the application of information technology IT in healthcare to improve individual and population health, education and research.
Concise, direct, but above all honest in recognizing the challenges in choosing and implementing an electronic health record in primary care, Electronic Medical Records: A Practical Guide for Primary Care has been written with the busy primary care physician in mind. Editorial Reviews. Review. From the reviews: “The current state of affairs in the national debate Buy Electronic Medical Records: A Practical Guide for Primary Care (Current Clinical Practice): Read Kindle Store Reviews - lirodisa.tk
The goal of the textbook is to stimulate and educate healthcare and IT professionals and students about the key topics in this rapidly changing field. About this book Physician adoption of electronic medical records EMRs has become a national priority. Show all. DesRoches, Catherine M. Pages Implementation Kashyap, Anupam Pages Maintenance and Optimization Wilkinson, Thomas M. Show next xx.
Recommended for you. PAGE 1. The way that information is entered into the EMR is important. The family physician researcher will want to evaluate their EMR system already in use according to this schema. First, a computer record generally provides the user with a number of options where the same information can be stored; for example, whether or not a pap test has been done may be found in the in-office examination section, the investigation section or with the laboratory results.
This means the family physician researcher must be very familiar with the software and scan all of these possible areas to find the data of interest. Second, EMR users may prefer to enter free text rather than be limited to picking items from drop-down lists for information such as the problem list, medication and referrals. Free-text data that are extracted from EMRs require coding to aggregate the data into specific variables. Some free-text data may not be searchable at all.
Fourth, medical record software generally provides memo fields, where users enter a long narrative for the visit note. Extensive use of these large blocks of text rather than coded fields makes automated analysis via queries difficult without a great deal of manipulation. As well, interpretation is a challenge and is time consuming, reminiscent of a classical chart audit.
In addition, most computer records allow storage or linking of digitized reports that are stored in the computer. The images are not analyzable; however, the typed text component may be read with optical character recognition software. While free text can be searched, this approach remains practically difficult. Fifth, clinical software generally makes only a minimal number of fields mandatory because all data are not required for all patients. Therefore, data that may be important for specific analyses are often missing, for example, a date that a referral was made.
However, there are ways to adjust for some of these situations. Some work can be done to address certain issues, such as recoding synonymous terms into one category. Additionally, elements can be added to software programs, such as the use of a medication database where all medications are named consistently and are updated regularly. Taking these steps can assist in making EMR data more useable for research purposes. Researchers using large EMR-derived databases have had to address at least some issues of data cleaning and standardization to ensure that complex research questions could be answered properly.
Once issues in accessing the data as a whole have been addressed, there are five basic options for searching for specific data in an EMR.
The availability of these features also depends on the type of EMR in use. In this case, users select a query option from the software menu pre-loaded into the EMR , giving the highest ease of use, but the lowest ability to conduct complex searches.
J Pain Res. In these cases, the data driven method described here is a reliable way to identify codes related to RA. Therefore, a table of the number of patients and the number of visits by physician per each user diagnosis category can be easily generated. Most of these studies used cross-sectional path modeling to examine the relationships between several PROMIS pain, mental, and physical health measures. Thus, knowledge about the real-world effectiveness of treatments for chronic pain is crucial to advancing the field and improving patient care. There would also be fewer callbacks from pharmacists with electronic prescribing. Little wonder.
For example, this type of query could produce a report that lists patients by diagnoses seen in the previous month. For instance, the user would select the patients by diagnosis that were seen within a specific start date and end date.
The user can, for example, generate a report of patients seen between a specific start date and end date and then select only patients taking medication A or medication B and not taking medication C. The fourth level involves using a special interface to enter structured query language SQL commands. The interface, if available at all, does not generally provide full SQL functionality. A user would be able to generate tabulations such as the number of patients by provider, diagnosis, age and sex and who have not been in the office during the 6 months prior to their last visit.
The fifth level of data extraction, using database tools, provides the highest level of ability to conduct complex searches and is the most challenging to use. Virtually any question can be answered if the data are in the EMR database. The user must have the EMR's entity relationship diagram that shows how various data files are related to one another.
The data dictionary that documents each element in the database with a description and data type is also necessary. As an example, with the database tools, users can generate a list of unique diagnoses, assign a category to each according to a particular research question and apply the categorization back to the EMR data. Therefore, a table of the number of patients and the number of visits by physician per each user diagnosis category can be easily generated.
Going beyond level three would likely require working with the specific EMR vendor to obtain the information that would allow this advanced level of data extraction. Alternatively, the vendor may perform the data analyses.
Both options may involve associated additional costs. Extracting and analyzing data using database software level five may involve collaborating with other PHC providers, researchers and information technology professionals since it often involves the creation of a separate database for analyses. Extracting data to answer less complex research questions is possible using the four levels described above.
However, with an increasingly complex research question, the fifth level of data extraction will likely be required. Many complex studies from the United Kingdom 15—17 and the United States 13 , 18 are based on EMR data which are extracted and then pooled using sophisticated methods to create large researchable databases. EMR data are generally stored either on-site within the computer server that supports the EMR or off-site by an information technology company. In the latter, patient records and associated data are accessed by PHC providers remotely over a secure network, such as a secure Internet portal.
Off-site EMRs require working with the information technology company to fully access the database.
The quality of the data in the EMR is an important consideration—poor quality data can negatively affect the results of research studies as well as the functioning of the EMR e. The first issue is for EMRs to better reflect co-morbidities than data based on one code per service as in a fee-for-service model of remuneration. The complete coding of all presenting problems is a pre-requisite for case ascertainment and for valid comparison of patients among practices where one must compare like with like and avoid comparing more complex patients with many co-morbidities with less complex patients with fewer co-morbidities.
The second key issue is developing criteria for identifying patients who have the condition to be studied. If one is interested in studying patients who have a particular condition for example, we need to have confidence that we can find these individuals in the EMR.
There are many places within the EMR that may need to be searched to identify these patients, including problem lists, billing codes, medication lists and physical examination results. There are additional considerations if family physician researchers wish to conduct a multi-practice study. After extracting data in a manner that acquires the same information on patients of each PHC provider, one must manipulate the data to answer the research question.
How many diabetic patients have regular HbA1C testing?