International Journal of Rheumatic Diseases 2010
ORIGINAL ARTICLE
Development, implementation and benefits of a rheumatology-specific electronic medical record application with automated display of outcome measures Anand N. MALAVIYA1 and Shashi B. GOGIA2 1 2
A&R Clinic for Arthritis and Rheumatism and Department of Rheumatology, ISIC Superspeciality Hospital, Vasant Kunj, and Sanwari Bai Surgical Centre, 28/31 Old Rajinder Nagar, New Delhi, India
Abstract Objectives: To make a rheumatology-specific electronic medical record (EMR) application for easy clinical data entry, automated display of outcome measures in real-time that generates well laid-out print-outs; and provides an easily retrievable database for clinical analysis and research. Methods: Highly labour-intensive ‘MS-WORD’ template used earlier provided the basic framework for developing rheumatology-EMR applications. The authors, a rheumatologist and a soft tissue surgeon with expertise in developing medical software, successfully created a rheumatology-EMR application over a period of 2½ years using the same basic flow of work as used in the old ‘MS-WORD’ template. Results: The resulting EMR application form has a standard medical record documenting demographic data, complete diagnosis, appropriate dates, visit number, disease status, history, physical examination, investigations, follow-up and prescription page (with automatic updates wherever applicable). Mathematical calculations required for outcome measures (DAS, DAS28, CDAI, SDAI, AS-DAS, BASDAI, BASFI, BASMI, SLE-DAI and others) are embedded in the software, with automated updating as the examination of the musculoskeletal system proceeds in real time. Following implementation of this EMR application, more patients are being seen, patient waiting lists have been reduced; more time is available for academic and teaching work, without compromising the quality of notes, and print-outs for patients. Data retrieval has simplified clinical research with increased numbers of abstracts being presented and research papers being published. Conclusion: Healthcare workers with understanding of the basic principles of computers and softwares should interact with software engineers who are either themselves medical doctors or are familiar with the workflow and clinical evaluation processes to create an efficient speciality-specific EMR application. Key words: automated outcome measure calculators, electronic medical records (EMR), rheumatology-specific EMR.
INTRODUCTION Rheumatologists mainly treat chronic diseases requiring lifetime management. An integral part of standard Correspondence: Professor Anand N. Malaviya, Flat 2015, Sector B-2, Vasant Kunj, New Delhi – 110070, India. Email:
[email protected]
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of care for such illnesses is meticulous record-keeping where sequential clinical assessment and laboratory data are easily retrievable, preferably in a tabular or a graphic form for quick and easy comparison of previous with the present values for decision-making. Keeping such records electronically is easy, tabular display is neat, amenable to easy access and data retrieval that is most helpful in clinical decision-making, guiding
2010 Asia Pacific League of Associations for Rheumatology and Blackwell Publishing Asia Pty Ltd
A. N. Malaviya and S. B. Gogia
Figure 1 Face sheet from a patient file prepared in MS-WORD template used prior to the availability of the present rheumatology-specific electronic medical record software.
treatment, and in clinical research and analysis. With widespread use of user-friendly electronic medical/ health software, this has become a practical option for most healthcare providers. However, like other superspecialities, rheumatology also has certain specific aspects that are not embedded in commercially available electronic medical record (EMR) software. These include certain specific medical history points, for example, clinical features related to the presence or absence of chronic inflammatory pathology in musculoskeletal tissue; pattern and topography of involvement; systemic, regional or local musculoskeletal problems; course of illness (chronic, acute, intermittent, palindrome etc.); and the use of different outcome indices for assessing disease activity in inflammatory arthritides, spondyloarthropathies, systemic lupus erythematosus and other common rheumatological conditions. Calculation of these indices often involves complicated mathematical formulae
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that deter most rheumatologists from using them routinely in clinical decision-making. After failing to identify affordable yet efficient software in the market with all the requirements of the superspeciality of rheumatology, in 2005 we decided to design software dedicated to the practice of clinical rheumatology. Here we report the steps in its development, its application and feasibility in a routine rheumatology clinic and objectively assessed benefits of its routine use. Obviously, there must be many individual rheumatologists or rheumatology groups in institutional settings or in practice around the world going through similar travails, finally deciding to create their own EMR dedicated to rheumatology. We recently came across similar efforts in creating a rheumatologydedicated EMR from a rheumatology group at Massachusetts General Hospital (MGH) in Boston.1 This report encouraged us to share our experience in this endeavour.
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Figure 2 Sample of sequential data recording earlier done on MS-WORD template used prior to the availability of the present rheumatology-specific electronic medical record software.
AIMS AND OBJECTIVE The aim was to make an EMR application dedicated to the practice of clinical rheumatology with the objective of providing a tool for rheumatologists for easy data entry related to clinical evaluation in real time, that saves time by automatic display of outcome measures in actual figures in real time, generates well laidout fully informative good-looking reports that have similarity to typed reports and prescriptions provided in the past. An additional objective was that it should provide an easily searchable and retrievable database. Last but not the least; it should avoid duplication of effort.
MATERIAL AND METHODS Background Rheumatology services were started in 2001 at the Indian Spinal Injuries Centre (ISIC) Superspeciality Hospital and at a private ‘A&R Clinic’, both in Vasant Kunj in South Delhi. Due to non-availability of any suitable EMR system dedicated to rheumatology, one
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of us (ANM) created a word processor-based template in MS Word. This template was a large (42 pages) document that had all possible information expected to be applicable to all possible rheumatological diseases. It had a standard format, that is, front sheet with fields for entering relevant fixed data along with demographic information. This was followed by pages with fields stating specific information pertaining to history related to most rheumatological diseases (chief complaints, present history, past, personal and social history, family history, drug and other treatmentrelated history), physical examination with specific features related to rheumatological problems (joint count, various assessment indices etc.) followed by prescriptions that contained general instructions, instructions related to physical/occupational therapy, diet and dieting, all the possible drugs that were expected to be used in rheumatological diseases, along with common adverse effects and their monitoring instructions. It had tables where the rheumatological findings were recorded in internationally recommended format sequentially at each visit. It also had tables that contained all the possible investigations
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Figure 3 Drug prescription instructions prepared on MS-WORD template used prior to the availability of the present rheumatology-specific electronic medical record software.
expected to be done in patients with rheumatological diseases where results could be entered sequentially for each clinic visit. There were tables for recording follow-up visits under a standard ‘SOAP’ (subjective, objective, assessment and plan) internationally accepted format. A separate table was provided for recording in-patient admissions. It also contained a ‘black-box’ that gave a concise summary of the medical–rheumatological problem under the headings mentioned above. With the help of a secretary, all the relevant clinical information pertaining to a patient were entered in the appropriate ‘fields’ painstakingly deleting all the redundant text from this large all-inclusive template, thus creating a medical record file of 4–5 pages (of Ariel 8-point font) that was specific to a
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particular patient. This tedious process took approximately 90 min to 2 h on an average to work-up a patient at the first visit. Data entry in the ‘Follow-up template’ at the follow-up visits took an average of 30–45 min. Although extremely time-consuming, the output had all details and patients felt satisfied. Figures 1–3 give samples of some of the pertinent pages from such a document on actual patients (identity hidden). However, as the document was created on a word-processor, data retrieval for publishing of papers was not possible as ‘Word’ documents are not amenable to statistics retrieval. Also, tracing each record was not easy. Therefore, a duplicate patient record was created simultaneously in a commercially available database program (Filemaker Pro), further adding the time taken for the work-
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up of a patient. Thus, the whole process had become extremely time-consuming and impractical for routine use in an out-patient clinic.
Development of software dedicated to the musculoskeletal system One of the authors (SBG), a soft-tissue surgeon, joined the rheumatology team to help patients needing his expertise. He has had a keen interest in designing and building medical software, had already made an EMR suited for general medicine, general surgery and paediatrics. He saw the work-flow using the cumbersome word processor-base template described above. Based upon his observations we started discussions on creating rheumatology-specific software using the basic software that he had already created with additions/ changes according to rheumatology requirements, primarily following similar approaches used in the wordprocessor template, described above. Over the next 2½ years with extensive input at each step of the software development, from a rheumatologist (ANM) with >35 years experience in clinical and research rheumatology, both in academic university hospital settings as well as private healthcare, the software was finally ready for clinical trial in a clinical setting.
Basic design of the software The development was done in Sybase Powerbuilder (V. 9.03, Sybase, Dublin, CA, USA) which runs on a client server architecture. This software has many tools for Rapid Application Design (RAD) which means that planning any additional innovation to complete deployment at the user end, including the complete cycle of testing and debugging, averaged around a week or two for any additional forms desired. The backend database is ‘Sybase SQL Anywhere 10’. The entire application runs on an ‘MS Windows’ platform. A separate server is recommended only for large-scale high-end users which was not required for our application. Total number of objects, that is, entry forms, subforms (including drop-down lists) as well as reports, are around 1200. Reports can be exported to any format including Excel, pdf (portable document format) and html (hypertext markup language – the language used for WebPages) and so on. A totally browser-based program was felt to be unnecessary at this time as it was expensive and was also found to slow the system down and consequently, our day-to-day operations.
Figure 4 Face-sheet tab – the first screen that opens on starting the rheumatology-specific software. From this page the record of any previously existing patient record can be opened or a new record creation can be initiated. Once selected, the essential details of the patient come into view. The rest of the tabs are linked to the selected patient.
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The application uses a large number of default entries as well as templates which are dependent on creation of master lists. While the entry format of the master lists and templates was done by SBG by mutual consultation, actual entry of the master lists was done by the rheumatologist (ANM). To ensure that the EMR is tamper-proof with total confidentiality, access is password-protected. Additionally, there are ‘Privileged templates’ for sensitive data e.g., prescription page. Thus the secretary/clinical assistant would be allowed access only to the face-sheet, investigation-entry page (and any other areas permitted by the rheumatologist) and not the others, further ensuring confidentiality.
RESULTS Face sheet Figure 4 is a screen-print of the face-sheet, the first screen that opens on starting the software. It has standard features of any medical record with several
features to make the demographic data entry semiautomated using the auto-entry tag that appears next to the selected ‘field’. Of special note is automatic ICD-10 (International Classification of Disease version 10 WHO – http://www.who.int/classifications/icd/icd 10updates/en/) disease code entry for primary and secondary diagnosis, comorbidities and inactive conditions. It has text space for automatic entry of any specific clinical characteristics of the primary diagnosis for that patient (descriptive). These are directly downloaded in the appropriate space from a disease-specific drop-down menu that appears automatically as the primary diagnosis is entered (Fig. 5). The face-sheet also has several additional components making it a near-perfect medical summary page. For example, it has a section stating ‘Disease status today’ that also provides disease status at the base-line as well as at the last clinic visit for comparison. It appears automatically as the patient evaluation is being carried out, in real-time. Figure 6 shows the print-preview of the facesheet that is printed and handed over to the patient at
Figure 5 Face-sheet details explained. Change in diagnosis automatically changes the tabs with relevant phrases related to that particular disease.
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Figure 6 Actual print-out of the face-sheet that is handed over to the patient at each visit. It contains diagnosis details, current (and selected previous) disease status created by an automated data selection process and the list of laboratory investigations that the patient must bring along at the time of the next visit.
the end of the clinic visit. Besides complete diagnosis and disease status, it also has the date and time of the follow-up visit along with the investigations that must be brought along at next visit.
Subsequent pages of the EMR On the left margin of the face-sheet are tabs to open the other pages in the electronic record file. Figures 7– 9 give the lay-out of some of these pages with examples of some of the drop-down menus available to choose from for automated data entry.
Embedded calculations related to outcome measures Embedded in the software are all the calculations for the various outcome measures routinely used in rheumatology practice. For rheumatoid arthritis, internationally accepted outcome measures included in the software are original disease activity score (DAS44),2 disease activity score based on 28 joints (DAS28)3
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with all its three versions, namely erythrocyte sedimentation rate (ESR)-based calculation, C-reactive protein (CRP)-based calculation, and where the value is calculated based upon any three measures, (excluding ‘patient global assessment’), that is, DAS-28-3; clinical disease activity index (CDAI),4 simplified disease activity index (SDAI),5 Bath ankylosing spondylitis-disease activity index (BASDAI),6 Bath ankylosing spondylitis-functional index (BASFI),7 Bath ankylosing spondylitis-metrology index (BASMI),8 Maastricht ankylosing spondylitis enthesitis score (MASES),9 ankylosing spondylitis disease activity score (ASDAS)10 and systemic lupus erythematosus-disease activity index (SLE-DAI),11 all calculated automatically using standard recommended mathematical formulae. Example is given in Figures 7 and 8. The values of these indices are displayed simultaneously (automatically) as data entry is being carried out in real-time. The same values also get automatically displayed on the face sheet. Based upon pre-determined cut-off val-
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Figure 7 The DAS-28 form includes a homunculus to select the affected joint and then a few more entries like ESR will calculate the DAS and update the disease status (referred to in Figs 4–6).
ues to categorise disease activity as ‘high’, ‘moderate’, ‘low’ disease activity or ‘remission’, this is displayed automatically on the face sheet. There is an option to choose which of the measures is to be used for classifying disease activity. For example, in patients with RA there is choice of DAS28 (ESR), DAS28 (high-sensitivity [hs]-CRP), DAS-3 (excluding patient global), CDAI (if recent ESR or hs-CRP values are not available), or SDAI. Similarly, in patients with spondyloarthritides there are options to use AS-DAS, BASDAI or DAS5344 for automatically classifying patients in different categories of disease activity; the switch between the preferred options for a particular patient was automatic based on the diagnosis (see blurb in Fig. 5). Being available sequentially in tables, the trend in these values can be quickly seen. Data entry for outcome measures like DAS28 was quickly performed using visual indicators (Fig. 7). A serial-wise entry was created for multiple visits which are also automatically updated into the face sheet as
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also mentioned in the report that the patient has provided along with the prescription. This was achieved without any duplication of effort. A similar table was used for spondyloarthritides including BASDAI, BASMI, MASES and BASFI (Fig. 8). These data are transferable to any standard spread-sheet software (e.g., Excel) for any type of graphic display and statistical calculations. It facilitates research and analysis directly from the available raw data at a fast pace. Figure 9 gives the sequential display of the laboratory data as they are entered.
List of drugs and the process of making prescriptions On opening the ‘Prescription’ page, one needs simply to type a few letters of the generic name of the drug to be prescribed; the full name appears automatically, which can then be selected. As soon as it is selected, options appear for selection of the preparation (oral/ subcutaneous injections), dose and so on. There is an
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Figure 8 The AS-DAS form is comprehensive with details of BASDAI, BASMI, BASFI as well as Maastricht enthesitis score (MASES). Similar to DAS, there are automated entries linked to the ‘Disease status’ displayed on the front sheet.
option to customise groups of drugs as ‘templates’ that are prescribed frequently for a certain disease with further saving of time. Entries to this are made faster through a ‘DRUG_MASTER TABLE’ at the back-end. The same needs manual entry of all the drugs that are anticipated to be prescribed before the software is introduced for routine use. This exercise is to be done only once unless additional (new) entries are required. Like all Master entries, here too a decision to update the master list is expedited by a small ‘M’ button that appears next to a list which is dependent upon a master list. Clicking that directly opens the relevant master table which is the ‘DRUG_MASTER TABLE’ for the prescription drugs list. The rows in the table include each drug, while the columns include the brand name, generic name, pharmaceutical name, dose and preparation, method of administration and any specific instructions/precautions related to the drug. In routine use, a selection of
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the generic name followed by its related nickname pastes all the related options of dosage, safety features and special instructions (e.g., when to take folic acid with methotrexate) to be entered in the prescription. The layout of the final printed prescription page is shown in Figure 10.
Requisitioning investigations and entering their results A ‘MASTER_TABLE’, similar to that containing list of drugs was created for investigations required. This consists of all the expected investigations that may be requested properly categorised under easily understood headings that can be selected from the drop-down menu (e.g., routine haematology, routine biochemistry, autoimmune serology, histopathology, imaging etc.). Like customisation available for groups of drugs, a similar option is available to make groups of investigations that are frequently used for specific diseases, both at the first clinic visit and a different list of inves-
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Figure 9 Sequential display of the laboratory reports prepared using this rheumatology-specific software.
tigations for the follow-up visits. Options are also available from drop-down menu for ‘urgent’, ‘to be done today’, ‘within the next few days’ or ‘before the next clinic visit’. There is also an option available to automatically make the same ordered list of investigations available with empty ‘results’ columns for entering the results as soon as they become available. At present, the entry of the results of investigations is being done manually, but an on-line link is feasible for automatic entry of results if and when the laboratories start providing that option. Of course there are many more features to this EMR that cannot be covered in this paper. Some similar features have been published previously.12–14
Implementation in the clinic Once the master tables were prepared, groups of drugs and investigations created, the process of training of the data-entry person to use the software was initiated. A secretary with basic training in computers could be easily trained within 2 weeks for efficient use of the EMR in routine rheumatology clinic work. A local-area network (LAN) was established between the personal computer (PC) at the reception area with the PC on the table of the rheumatologist’s office. The patient would first present at the reception. The secretary or clinical assistant would make all the entries of laboratory tests, vital
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signs and so on. Then the patient moves to the rheumatologist’s room where the same file is opened on the rheumatologists’ PC. As the rheumatologist proceeds with the clinical evaluation and examination, the secretary or the clinical assistant carries out entries, in real time. The evaluation details appear instantaneously for therapeutic decision-making (Figs 4, 5, 7–9).
Benefits of the implementation of the software The implementation of the software has demonstrated benefits that can be gauged objectively as shown in Figures 11–14. The patient work-up time at the first visit has been reduced on an average from 2 h to 30 min. Thus the number of patients seen has gone up, cutting down the waiting list from several weeks to a few days only. A major benefit has been in the evaluation of follow-up patients where the time has been shortened to almost 10–15 min. Another major benefit of the EMR implementation has been its immediate impact on clinical research. Data for writing two research papers on ankylosing spondylitis could be easily extracted by a Visiting Rheumatology Fellow from USA during his summer vacation in India.15,16 Another paper based on data extracted from this EMR has been published recently.17 Several additional papers are in the process of being written.
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Figure 10 Actual print-out of the prescription that is handed over to the patient at each visit. It is made simply by selecting and clicking the desired drugs; it can be automated through disease-based templates (required for first prescription only). Revisit prescriptions only need a click to discontinue a particular drug or to add a new drug.
DISCUSSION There is evidence that while computers are being used increasingly by medical administrators, medical accountants, inventory personnel and other supportive services, it is still not widely used for patient-related data entry, for example medical aspects of patient care, including recording history, physical examination and decision-making, in real time in the clinical setting.18–24 The situation could be much worse in developing countries. It is well known that among all professionals, practitioners of medicine are lagging far behind in using computers and specific software for clinical purposes.18–20,22–24 There could be several reasons for this problem.21,25–28 Although not well documented, generally colleagues feel intimidated by the way com-
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puters work, especially the demanding ‘exactness’ – any command given to it must be absolutely exact. The second problem, often not emphasised, is the inability to use keyboards due to lack of expertise in routine typing. These two problems could be the main reason for a lack of confidence among doctors in general computer use in daily life. Also, medical practitioners are generally individualistic in their style of clinical workup of the patient that they have become used to over years in clinical practice. This leads to resentment if the work-flow is changed in any way. Doctors who do work with software have had to go through a learning curve of using it and in that phase tend to be either over-swayed by the technology – with some patients genuinely complaining that ‘He was looking more at the screen than me’ or being
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(a)
(b)
Figure 13 Graph depicting time-saving in ANM’s private clinic both for the work-up of first visit as well as for revisit patients.
Figure 11 (a) Graph showing the number of patients seen at the once-a-week ‘Combined Rheumatology Clinic’ at ISIC Superspeciality Hospital (held on every Friday). As can be seen the numbers rose rapidly after installation of the software after a slight dip during the installation period. (b) A similar graph but based upon average number of patients per clinic day (i.e. total points seen for 6 month period/No. of working Fridays i.e. removing the Fridays that were Holidays). It can be seen that the ‘dip’ in the number of patients seen during the transition period when the software was installed and the staff was getting used to it (upper panel), was negligible.
Figure 12 Graph depicting the shortening of total clinic time despite larger numbers of patients being seen in the clinic (shown in Fig. 11) allowing time for an academic seminar over lunch, every week.
intimidated by their lack of understanding of how to get the exact piece of the information they are looking for.
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Figure 14 Graph showing the number of patients seen before and after the installation of the rheumatology-specific software. The number has increased as we have started giving more appointments. Also the patient visits are cleared by 3 : 30 pm as opposed to 4.00 or 4 : 30 previously. A waiting list of over 3 months with some appointment refusals have now been cut down to less than a week with almost no refusals.
There is evidence that educating the users, that is the doctors, back and front office workers, as well as nurses, in software usage is important.13 The benefits of implementing specifically dedicated EMS have become obvious with us and is also reported by others.1,27,29 Waiting lists of new patients has been drastically reduced; the number of patients seen during clinic time has jumped without any reduction in the degree of patient satisfaction. Figures 11–14 show the impact of EMR in increasing the efficiency of the rheumatology clinic. The print-outs being handed over to patients are neat and well laid-out containing
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all the necessary information properly displayed. As already mentioned above, its impact on clinical research has also been almost immediate with three clinical papers already published13–15 and several additional papers under preparation. Overall our work experience has been extremely rewarding and patients are greatly satisfied to have a doctor who apparently has all their data on the computer screen even before they walk in. A printed sheet similar to the one they once received before the implementation of the new software, was still being given that was neater and easier to understand; thus their level of satisfaction has been maintained. It is necessary to point out certain real difficulties and disadvantages of total dependence on EMRs. In our experience the critical step in the successful implementation of rheumatology EMR was the use of a data-entry person (either the secretary or a clinical assistant, specialist nurse) to keep entering the physical findings, especially the joint count and spinal indices, while the rheumatologist was actually performing the examination in real time. Non-availability of such an assistant may be a real problem in some situations (cost factors etc.) and could become the main cause of failure of EMR implementation. There are several additional potential logistic difficulties in proper functioning of EMR application, for example frequent power failures (especially in a country like India), equipment breakdown, occasionally problems with computer viruses (does not effect this software per se but the entire operating system may be affected when nothing works); and a loose LAN network connector could ring alarm bells. Presentation of history and other forms of long text once automated will become broken and singularly lacking in grammar that may not please some medical professionals. Last but not the least, computers/software/training needs are expensive. Although some of these are general problems, many have been overcome by us due to sheer years of usage and development experience by the authors – 15 years in the case of one of the authors (SBG).
ACKNOWLEDGMENTS Authors would like to thank the ‘Rheumatology Team’ at ‘A&R Clinic’ and at ISIC Hospital for enthusiastically accepting the newly created rheumatology EMR application in day-to-day work; especially Miss Pinky Negi and Mr. Prashant Deshmukh, the medical secretaries; Mr. Qamar Zaheer, occupational therapist; and
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Miss Roopa Rawat and Miss Sadhna Singh, rheumatology nurses, for their constant help during the clinical evaluation of patients. Authors would also like to thank Mrs. P. Bhandari for her help in painstakingly converting most of the old template format into a workable EMR application.
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11 Urowitz MB, Gladman DD (1998) Measures of disease activity and damage in SLE (SLE-DAI). Baillieres Clin Rheumatol 12, 405–13. 12 Gogia SB. Methods for faster and efficient data entry in ‘DataWindows’http://pbdj.sys-con.com/node/1064095. 13 Safran C (2009) ‘Informatics training for clinicians is more important than hardware and software’. In: Hutter M (ed) IMIA Yearbook for Medical Informatics, pp 164–6. Schattauer, Stuttgart. 14 Meghan Daum. The societal scourge of e-cards (Quoted from the LA Times) The Indian Express 26th December 2009 pp 13. 15 Aggarwal R, Malaviya AN (2009) Diagnosis delay in patients with ankylosing spondylitis: factors and outcomes–an Indian perspective. Clin Rheumatol 28, 327– 31. 16 Aggarwal R, Malaviya AN (2009) Clinical characteristics of patients with ankylosing spondylitis in India. Clin Rheumatol 28, 1199–205. 17 Malaviya AN, Agarwal D, Sharma A, Gogia SB, Zaheer Q (2010) Practicing computer-aided objectified outcomedriven targeted treatment of rheumatoid arthritis (RA) in a resource constrained country: Results from a single rheumatology clinic. Ind J Rheumatol 5, 3–1. 18 Jha AK, DesRoches CM, Campbell EG, et al. (2009) Use of electronic health records in U.S. hospitals. N Engl J Med 360, 1628–38. 19 Jeff. When will doctors enthusiastically get and use EMR software and EMR systems? in website ‘Welcome to EMR and HER’Available at http://www.emrandehr.com/2009/ 07/22/when-will-doctors-enthusiastically-get-and-use-emrsoftware-and-emr-systems/ retrieved January 2010.
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20 67% of Physician Offices Do Not Use Electronic Medical Records (EMR) Software, says SK&A in website (SK & A Information Services, Inc.) http://www.marketwire.com/ press-release/Sk-and-A-Information-Services-Inc-970921. html retrieved Apr 06, 2009 retrieved January 2010. 21 Wilson JF (2009) Making electronic health records meaningful. Ann Intern Med 151, 293–6. 22 Calvan BC. Few physicians using electronic prescribing in California in website The Sacramento Bee (Health and Fitness)http://www.sacbee.com/healthfitness/story/1650688. html. 23 The Medical News Survey indicates low EMR use among U.S. physicians http://www.news-edical.net/news/2009 1218/Survey-indicates-low-EMR-use-among-US-physicians. aspx Retrieved December 2009. 24 Gill JM (2009) EMRs for improving quality of care: promise and pitfalls Fam Med 41, 513–5. 25 Baron RJ, Fabens EL, Schiffman M, Wolf E (2005) Electronic health records: Just around the corner? Or over the cliff? Ann Intern Med 143, 222–6. 26 Basch P (2005) Electronic Health records and the National Health Information Network: affordable, adoptable, and ready for prime time? Ann Intern Med 143, 227–8. 27 Chaudhry B, Wang J, Wu S, et al. (2006) Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 144(10), 742–52. 28 Halamka JD (2006) Health information technology: shall we wait for the evidence? Ann Intern Med 144(10), 775–6. 29 Bates DW (2009) The effects of health information technology on inpatient care. Arch Intern Med 169, 105–7.
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