How To Design An Electronic Medical Record

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1 65 Users Needs and Expectations of Electronic Medical Record Systems in Family Medicine Residence Settings George Demiris a, Karen L. Courtney a, Steven E. Waldren b a University of Missouri-Columbia, Missouri, USA b Center for Health Information Technology, American Academy of Family Physicians, USA Abstract While physician informational needs have been examined in the literature, rarely do the findings have the specificity required to drive development of Electronic Medical Records (EMRs) features. Using Delphi methodology, a comprehensive list of desirable, ranked EMR features was developed for physician residency practices. The identified EMR features and implications for system development are explored in this paper. Keywords: Medical Record Systems; Computerized; Internship and Residency; Delphi Technique; Medical Informatics 1. Introduction Informational needs of physicians have been studied in the scientific literature [1,2]. Many of these studies present needs at a more abstract or high conceptual level. What is lacking in the literature is an understanding of these needs at an implementable level. An implementable level is defined as the granularity or degree of detail sufficient for the developer of an Electronic Medical Record (EMR) system to devise a specification to meet that need. For example, stating that a user s need is to communicate with other users does not provide information at an implementable level. The developer must still decide whether this communication should take place in real-time or in an asynchronous matter and whether it may be text or voice based. In contrast, the need for secure messaging would be a possible implementable need; as the developer can infer that the communication will be text-based and asynchronous. Partly responsible for this lack of design directives at this level, is the difficulty of defining and assessing users needs and change over time. As individual physicians practice, their informational needs change, and medicine itself is constantly changing by introduction of new information. Another confounding factor is the heterogeneity of physicians. The needs of a primary care physician are not the same as those of a specialist. In addition, the practice setting can affect need, such as urban vs. rural or private practice vs. residency practice. The varied system requirements in different medical environments have been identified as a major barrier to adoption of Electronic Medical Record (EMR)

2 66 systems [3], yet little is known about the unique system requirement of specific medical settings. An EMR system can meet physicians informational needs better than paper-based records[2]. In spite of the perceived benefits of an EMR and the recommendations of the Institute of Medicine[4], the use of EMR systems continues to be low. In a residency setting, where technology innovation is expected and physicians are trained for clinical practice [5], EMR usage was less than 20% in 1998 and was expected to be still less than 50% in 2000 [6]. Lenhart et al. also found non-emr users viewed the capabilities of EMR systems significantly more optimistically than users[6], indicating many EMR systems fail to address users expectations. There is limited knowledge of physicians needs and expectations of an EMR from the corpus of physicians at the level specified above. Less is known about the subgroup of physicians in a residency setting. Musham et al. studied the perceptions of family practice educators regarding EMRs [5]. They discovered that at a high level the users needs were greater efficiency in delivering care, lowering costs, and improving quality of care. Their findings were not at an implementable level, which could empower user-driven EMR design for residency programs. A residency practice has added dynamics that must be addressed on top of the issues of private practice. Residency practice encompasses almost all of the aspects of other practice settings, but it has different priorities. For example, research is of greater importance in a residency practice; also, the business model is different. Residencies are usually affiliated with a university and/or a large hospital and are frequently non-profit. This is in contrast to private practice where many are not affiliated with a university or hospital and if so, are profit-driven. On top of these aspects, other issues such as a more complex patient-provider model (e.g. a billing physician, a supervising physician, and a resident physician for a single patient visit) and resident education have an impact on informational needs. Once we have an understanding of physicians informational needs at an implementable level, we can facilitate a user-driven EMR system design process. The purpose of this study is to provide the framework for user-driven design of EMR systems for family practice residency programs that will bridge the gap between high-level informational needs and implementable features. 2. Methods In order to determine family practitioners needs and expectations of an EMR system, we conducted an extensive content analysis of published literature and a Delphi study with directors of family practice residencies. Content Analysis A literature review was performed to identify those articles that addressed EMR features or feature lists. The search strategy was a search of Medline from 1996 to March 2003 (week 1). We combined the MESH term Medical Records Systems, Computerized with the key words, choosing, feature, attribute, and resident. The articles were reviewed by title and abstract, if available, and those pertaining to EMR features were used. This resulted in ten articles[8-17]. The authors reviewed the articles and extracted features for an independently developed EMR feature list. These lists were then merged to form a new

3 67 master feature list. Inter-reviewer reliability was not a concern since a comprehensive list was desired. This list was then used as the designed questionnaire for the Delphi method. Delphi Method A Delphi method may be characterized as a method for structuring a group communication process so that the process is effective in allowing a group of individuals, as a whole, to deal with a complex problem. [7] Delphi methods can be separated into blank questionnaire or designed questionnaire approaches. The blank questionnaire approach presents the participants of the method with only the problem being addressed, in this case what are the features of an EMR that satisfy your needs and expectations of an EMR. The designed questionnaire gives participants starting data about the problem, in this case a list of features from the literature. The main advantage of the latter approach is a lower level of effort on the part of participants. In our case, the participants did not have to originate a de novo list of features. The disadvantage of this approach may be a possible bias of respondents. In our case, the scientific approach of a content analysis for the designed questionnaire would limit this bias. Directors of American Family Medicine residencies, who belonged to the Association of Family Practice Residency Directors listserv, were asked to participate if they had an interest in EMR systems, products and/or design. The list contains approximately 330 unique addresses. The number of unique directors or programs in the list is unknown. This listserv has been in existence for several years and has an active membership. The mode of communication with the individuals included messages in addition to an initial face-to-face meeting with a subset of individuals [8]. This meeting was by convenience due to a previously scheduled directors meeting by the American Academy of Family Physicians. The model consisted of three iterations. The first iteration started with a list of EMR features developed from the content analysis. Each individual was asked to add relevant features to the list and to remove irrelevant features. In order to increase face validity of the instrument, we asked several experts in health informatics, electronic medical records and questionnaire development to review the list. Content validity was addressed as the instrument was designed based on the content analysis of published literature. The questionnaire had a Flesch Reading Ease of 68% and a Flesch Kincaid Grade Level of 9. After respondents provided us with suggestions to add new features and/or remove existing ones that they rated as irrelevant or of less priority, a new list of features was developed from these modified lists. For the second iteration, the new list was sent to each individual to rank each feature on the list on a scale of 0-5 (Table 1). To address unranked features, the average of all rankings for that feature was used for the missing rank score in order to correct the cumulative score. Features with a cumulative importance score being one standard deviation above or below the mean were extracted. In the third iteration, each individual was asked to comment on these features in order to confirm agreement with the classification of features with the highest and lowest importance. Table 1 - Importance Scale 0 Would have a negative impact on the practice of medicine in a residency 1 Would have no impact on the practice of medicine in a residency 2 Would have little positive impact on the practice of medicine in a residency 3 Would have a big positive impact on the practice of medicine in a residency 4 Would be critical to practice medicine in a residency 5 Would be mandatory to practice medicine in a residency

4 68 3. Results A total of 30 directors participated in the study. The types of residencies represented included: (1) community based, unaffiliated with a university, (2) community based, university affiliated, (3) community based, university administered, and (4) university based. The participating directors represented residency programs located throughout the United States. The first iteration received a 37% (11 respondents) response rate; 5 of these 11 individuals proposed a modification of the list. Eight additional features included: (1) assignment of an attending and a resident as primary physician, (2) variable billing physician, (3) ability to generate consult letter from note, (4) digital signatures, (5) ability to import data into note, (6) resident reports, (7) ability to link information directly into the chart but not be part of the legal chart, and (8) document delinquency tracking. The feature removed was resemblance to a paper chart. This resulted in a new list of 74 features. The second iteration received a 57% (17 respondents) response rate. The average importance score for all features was 3.57 ( % CI). The cumulative average score for all features was with a standard deviation of Eleven features were above one standard deviation of the mean and ten features were below (Table 2). Table 2-Ranked Features FEATURE AVG 95%CI Highest Importance Features Access for multiple, simultaneous users System Reliability Meets Regulatory Requirements Rapid access to patient data User-friendly (Intuitive user interface esp. for rotating residents and students) Secure Remote access (Portability) Decreased clinical errors Digital Signatures (esp. to sign off resident notes) Increased legibility Compatible with existing computer systems Integrated clinical reminders Lowest Importance Features Allows add-on products Open Source Decreased doctor time per encounter Import pictures and drawing capabilities Support for develop of vocabulary Available for multiple Operating systems Voice recognition Integrated consumer databases Contract management Available as Application Service Provider (ASP) In the third iteration there was agreement on the features in the highest and lowest importance category (15 respondents). The two most common comments were (1) the need to define the features and (2) although features were in the low importance group, they have a place in the respondents practice.

5 69 Individual features can be clustered into four logical groups: fast and easy access; research; EMR system properties; and low cost. The fast and easy access grouping is a cluster of features such as: access for multiple simultaneous users; secure remote access; PDA access; web-enabled access; rapid access; and user-friendly access. The access cluster has an average score of The feature cluster labeled as Research with an average score of 3.53 is a composite of features such as data mining; data warehousing; support for clinical trials and increased opportunity for research. The feature relating to vendor or EMR system properties, which encompasses high vendor creditability, meets regulatory requirements, online support, high level of support, and system reliability, has an average score of Low cost, which includes low implementation cost, increased staff efficiency, increased cost savings and profits, low hardware requirements, and low maintenance costs, has an average score of Discussion The response rate was typical of Delphi methods except for the increase in the second iteration. This could be due to the relative lower effort needed of participants for the second iteration than the first. If so, this validates our choice of the designed questionnaire approach to increase response rates. The high average importance score implies participants believe all listed features are important. The majority of highly ranked features can be categorized as core functionality of an EMR system. This implies that EMR design efforts should focus on improving core functionality instead of developing more Bells and Whistles. Musham et al. found high cost as a major concern for residencies [5], but those features which correspond to lowering cost (i.e. low implementation cost, increased staff efficiency, increased cost savings and profits, and low maintenance costs) were in the middle to lower half of the ranked list. The reason for this is uncertain. It could be the view of cost as a barrier and not a feature, or the fact that regardless of cost, an EMR system is perceived as not worth implementing if it does not possess important features. Looking at the 95% confidence intervals for those features in the highest versus lowest categories, there is greater internal reliability with the highest importance features; however internal reliability of the lowest rated features was acceptable. Also the lowest importance features tended to be more technical in nature. We believe one reason for this could be a lack of knowledge about these technical aspects across all participants. As users experience and knowledge of an EMR increases their needs and expectations will change. 5. Conclusion These implementable features represent the needs and expectations of family practice residency users. These needs must be met for EMR systems to be adopted and utilized by users. A major limitation in identifying important EMR features is the lack of a common terminology or ontology. Formal definitions, and in many cases one working definition, do not exist. This makes it difficult for individuals to discuss features and be confident that the other individual is thinking of the same functionality. There is a great need to develop such a terminology and ontology. [18] Our data provides the ability for user-driven design of EMR systems for family practice residency programs. These data suggest that core functionality is of greatest importance for residency program users. Special attention to the underlying needs of the target user

6 70 population is paramount. In the case of a family practice residency program, a complex patient-provider model and resident education tracking appear to be decision-driving factors. A Delphi method can be useful in delineating the feature list and underlying decision-making model for different target populations. 6. Acknowledgements This work was supported in part by the National Library of Medicine Biomedical and Health Informatics Research Training Grant T15-LM References [1] Strasberg HR, Tudiver F, Holbrook AM, Geiger G, Keshavjee KK, Troyan S. Moving towards an electronic patient record: a survey to assess the needs of community family physicians. Proc AMIA Symp 1998; [2] Dumont R, van der LR, van Merode F, Tange H. User needs and demands of a computer-based patient record. Medinfo 1998; 9 Pt 1: [3] Schoenbaum SC, Barnett GO. Automated ambulatory medical records systems. An orphan technology. Int J Technol Assess Health Care 1992; 8(4): [4] Institute of Medicine Committee on Quality of Health Care in America. Crossing the Quality Chasm: A New Health System for the 21st Century. Hardcover ed. National Academy Press, [5] Musham C, Ornstein SM, Jenkins RG. Family practice educators' perceptions of computer-based patient records. Fam Med 1995; 27(9): [6] Lenhart JG, Honess K, Covington D, Johnson KE. An analysis of trends, perceptions, and use patterns of electronic medical records among US family practice residency programs. Fam Med 2000; 32(2): [7] Linstone H, Turoff M. The Delphi Method: Techniques and Applications. Addison-Wesley Publication Company, [8] Welch JJ. CPR systems: which one is right for your organization? J AHIMA 1999; 70(8): [9] Ury A. Choosing the right electronic medical records system. Cost Qual Q J 1998; 4(1):4-6. [10] Aaronson JW, Murphy-Cullen CL, Chop WM, Frey RD. Electronic medical records: the family practice resident perspective. Fam Med 2001; 33(2): [11] Silver D. Doing away with paper. Part 2--Starting up your new system. Aust Fam Physician 2002; 31(6): [12] DeBry PW. Considerations for choosing an electronic medical record for an ophthalmology practice. Arch Ophthalmol 2001; 119(4): [13] Silver D. Doing away with paper. Part 1--Advice for setting up fully computerised medical records. Aust Fam Physician 2002; 31(6): [14] Holbrook A, Keshavjee K, Langton K, Troyan S, Millar S, Olantunji S et al. A critical pathway for electronic medical record selection. Proc AMIA Symp 2001; [15] Smith WR, Zastrow R. User requirements for the computerized patient record: physician opinions. Proc Annu Symp Comput Appl Med Care 1994;994. [16] Shortliffe EH. The evolution of electronic medical records. Acad Med 1999; 74(4): [17] Matthews P, Newell LM. Clinical information systems: paving the way for clinical information management. J Healthc Inf Manag 1999; 13(3): [18] EHR Collaborative. Public Response to HL7 Ballot 1 Electronic Health Records Address for correspondence George Demiris, PhD, University of Missouri Columbia, 324 Clark Hall Columbia, MO USA, demirisg@missouri.edu