She stated that medication dosage was an exception most of the time because they are more variable. Nurse A used this shortcut for documentation as one way to get her charting completed in the EHR before the end of her shift.
This practice by Nurse A and other nurses from Mental Health Center A resulted in a large focused review conducted by the Medicaid Fraud Division along with fines and penalties for payment for care that was not rendered at the level of service claimed.
The EHR has specific patient safety and documentation integrity tools built into its design. Memorial provides orientation to all medical students and residents providing patient care services on how to use the tools for accurate and complete documentation. Because it is very important that only those services personally provided or supervised by teaching physicians generate a bill for services, the computer-generated templates guide all of the participants in patient care to the correct place and format for recording observations within the record.
Teaching physicians must sign on to the system so the appropriate authentication is attached to their chart entries, and any templates must be modified to reflect specific conditions and observations unique to the service. Teaching physicians must be physically present to report services for health plan claims.
Alerts are generated when a copy or paste function is used warning the EHR user about plagiarism and the risk of copying documentation out of context in a legal document. Medical Center A also created a full slate of documentation guidelines, policies, and procedures surrounding use of the EHRs and related tools for capturing information.
Special emphasis was placed on the prohibition of pulling forward information from previous visits as a basis for increasing the level of evaluation and management for billing. There are now clear protocols about the completion of an entry or record—when information displays or not to users and when the record gets locked down for either pulling forward or copying text content to another location.
Situations and examples are provided that describe the appropriate use of pulled forward and copied entries taken from other sources. Policies about the use of scribes or surrogates making entries in an EHR are created and monitored for compliance. All designated scribes or surrogates have the ability to create entries but require countersignature authorization from the supervising clinician before they display to other users of the EHR system. Mental Health Center A also started a clinical documentation improvement program that included appropriate use of nursing documentation templates suitable for recording medication management.
These templates create the framework for required documentation unique to each patient. They include built-in edits to ensure correct recording of dosages by comparing nurse entries with the issuing pharmacy instructions and the original scripts.
A wide spectrum of data is collected in healthcare and must be collected accurately, completely, and consistently.
Data integrity is of extreme importance because it is used to identify and track patients as they move from one level of care to another. Data are used to verify the identity of an individual to ensure that the correct patient is receiving the appropriate care and to support billing activity. Ensuring the integrity of healthcare data is important because providers use them in making decisions about patient care.
The scenarios below are examples of worst case and best case examples associated with data integrity. Because of the large amount of data collected in healthcare, data integrity can be compromised repeatedly. Information can be entered incorrectly or in incorrect formats in various healthcare settings, so procedures must be defined to ensure that data are collected consistently regardless of the medium being used.
A patient was seen by a clinician on September 1, , just before lunch. Once the patient was examined, the clinician got sidetracked and was not able to enter his note on the date the patient was seen. During the visit, the patient discussed a possible reaction to a prescribed medication. On September 5, , the clinician was back on duty after a long weekend; upon review of the record, he realized that he did not make an entry on September 1, As the clinician began documenting, he decided that he wanted the date to reflect the actual date the patient was seen.
He changed the date to September 1, , at a. He proceeded to enter the documentation as best he could. He remembered and documented the symptoms the patient described surrounding the potential medication reaction. When another clinician reviewed the record, he saw the new note. This second clinician worked over the weekend and did not recall seeing this information but sees now that the date displayed is September 1, , at a.
This alarmed the clinician, as he prescribed the medication that the patient had indicated a possible reaction to in the past. A facility has multiple biomedical peripherals connected to the EHR such as portable ECGs and intravenous infusion pumps.
The main system has a synchronized clock for display with date and time stamping on notes, laboratory results, etc. Some payments are tied to quality of service. Indicators for chest pain include requiring that the ECG be performed within 10 minutes of arrival in the emergency room. A patient is brought to the emergency room at on September 1, An ECG is started and completed according to orders entered at on September 1, The ECG is uploaded, read, and interpreted.
At on September 2, , the clinician completes her documentation of the assessment and orders admission for acute myocardial infarction. After a retrospective review of the case, the ECG is reported as being ordered at but not completed until September 2, , at This is 15 minutes after the note entered by the clinician stating the ECG was done and showed ST-elevation myocardial infarction. Not only has this case fallen out for performance measures but it will also have difficulty standing up in court.
It could possibly fail a third-party review if the outpatient was treated and released because the chest pain was thought to be gastrointestinal in nature. An audit might determine the ECG was not a covered service if done after the time of discharge. The linkage of peripherals needs to have the clocks on each system synchronized to support the integrity of the data collected for the care provided. A special feature of the software ensures optimal reimbursement for skilled beds through a point-of-care system that prompts nursing personnel to enter data elements.
The nurses and nursing assistants enjoy the convenience of the touch pad technology and the time the new system saves them for charting. However, the director of nursing has discovered that the system is creating documentation inconsistent with actual patient conditions.
The MDS being transmitted to CMS is overstating the type of care for therapy units and suppressing one of the reportable quality indicators residents with pain. The documentation in the records supports the optimized payment from Medicare for the skilled-care patients, but the director of nursing is very concerned about the consequences of using it. The ability to make amendments to the EHR is defined by business rules and policy.
Entry errors are defined and reported accordingly. The EHR has specific patient safety and documentation integrity tools built into the design. University Hospital A provides an orientation to all medical students and residents on how to use the documentation tools so the information collected is always accurate and complete.
It is very important that only those services personally provided or supervised by teaching physicians generate a bill for services. Medical necessity and intensity of service documentation are unique to each visit. Alerts are generated when a copy or paste function is used to warn the end user about plagiarism and the risk of copying documentation out of context in a legal document.
The date that a note is entered into the EHR is hard coded. However, clinicians have the ability to associate the note with a date of service to reflect a reference date of when they saw patients as well as an indication of a late entry. Both of these dates are important to best practices in HIM. The facility made a conscious effort to ensure a standard for date and time stamps.
To accomplish this goal, the facility inventoried all interfaced applications and biomedical equipment. Each equipment vendor was contacted to determine the best method of synchronizing peripherals to the main system, which minimized or eliminated users having to keep track of the time themselves.
However, some equipment may need to be checked at the beginning of shifts or at as the staff do with crash carts, etc. A special feature of the software ensures optimal reimbursement for skilled beds through a point-of-care system that prompts all personnel to enter data elements. Each section of the MDS requires various personnel to provide coded data supported by their patient-specific documentation in the EHR.
Clinical, nonclinical, and medical staff have all found the convenience of the touch pad technology to be a time savings for both charting and completing their portion of the MDS. The software for collecting the MDS data has built-in hierarchy for the user physician or nurse assistant and for most data elements.
The orders section in an EHR can be a large database. Prescriptions must have specific fields associated with them to identify the details of the individual order—which physician placed the order; the date, time, reason, or diagnosis associated with the medication; status, etc.
The provider may document a diagnosis that attaches itself to a template note. Thus, the diagnosis in the EHR template note might be different than what was coded and billed. These case studies have been prepared along with guidelines to provide further references.
See guidelines Failure of an EHR system to provide appropriate safeguards against medication errors, including the wrong patient, the wrong drug, or failure to consider all available data, can contribute to poor quality care. Examples of automated patient registration data elements and patient safety issues illustrate the need for identity management safeguards.
Rogers is ordering a prescription by using electronic order entry for a nursing home resident in the geriatric outpatient clinic at City Hospital A on October The patient with dementia presents to the clinic with a nursing assistant from Nursing Care Facility A, she is registered as Ethel Mertz, and her health records are placed in queue for Dr. Nursing Care Facility A had been contacted the previous day to gather information for the appointment. The City Hospital A system automatically populates registration data and places patient records in an authorized access queue for scheduled patients in the clinics on the day of the visit.
The nurse has downloaded a printout from the EHR system for Dr. The clinic staff has already verified that Ethel is eligible for Medicaid. The physician order entry software provides the capability for default self-selection upon entering the first three letters of the drug. The physician wanted to order Norfloxacin for an eye infection. Both are oral medications, although muscle tightening or spasms could result from Norflex.
The order was signed electronically, the medication was made available for the nursing assistant to pick up, and the patient was returned to the nursing facility. The patient with an infection requiring treatment with Norfloxacin began taking Norflex and returned to the emergency room later the same week with septic shock due to a very serious bacterial infection of the left eye. When the emergency room staff accessed her health record, there was no entry for a geriatric clinic visit on October 15, so the findings from her care were not available.
City Hospital A filed a Medicaid claim for Ethel Mertz and was paid for a clinic visit on October 15 with pharmacy charges for a Norflex prescription. She has a number of chronic health problems, takes a number of medications, and has an allergy to drugs containing quinolone.
The physician selects another type of antibiotic that is equally effective and avoids the risk of an adverse reaction. A nursing home resident presents to the City Hospital A geriatric clinic with Staphylococcus aureus conjunctivitis. The nursing home had arranged the appointment with Dr. Rogers by using an online registration portal that requires verification of five critical demographic data elements to establish patient identity. Because there are two patients with similar names at Nursing Facility A, the home is careful to make sure that this patient, Mrs.
Ethel Merts, is registered with her physician Dr. Her current medication list, problem list, and allergies are uploaded to the system from the nursing home EHR. Rogers has a printout of the nursing home records at the time of the examination. At any time when verification is required, Dr. Rogers is able to access the full EHR including the uploaded information provided by the nursing home.
The following guidelines provide recommendations for organizations to reduce the likelihood of fraud when EHRs are being used. An organization communicates its ethics and commitment to complying with laws and regulations through its policies. Organization-wide policies that should be established to reduce the likelihood of fraud include the following:. This is a list of highly recommended policies and is not meant to be exhaustive. Organizations implementing an EHR may need to develop additional policies according to their needs.
Education programs need to address the different functionality of an electronic versus a paper environment specifically for individuals who have previously worked in a paper health record environment. EHR users more than likely will continue to use paper records along with the EHR, so distinctions regarding the unique fraud risks of the EHR must be conveyed. In the paper environment, data are usually static, and alterations or changes to documents are more readily apparent.
In the EHR, alterations can more easily go undetected, and errors can grow exponentially. EHR fraud prevention education programs should address:. Each entry that is not solely authored by the user must be validated by the user in a manner similar to that for bibliographic notations and include the name, date, time, and source of the data.
This requirement can be satisfied by system software design that routinely provides this validation. Compliance with these elements will ensure that the requirements for regulatory agencies and payers will be met. Further reference should be made to various federal and state legislation and the requirements of various oversight agencies:.
Note: the program and content outlined above are recommended as a starting point for organizations. Modifications and additions should be made as appropriate to meet organizational needs. An audit trail is a business record of all transactions and activities, including access, that are associated with the EHR. Facilities using electronic health information systems need to ensure that individuals entering information into the EHR are aware that system audit trail functionality is in place allowing them to legally access, amend, retract, correct, or edit entries that were made during the normal course of business, at or near the time the care.
There are reasons other than documentation and fraud and abuse concerns that would encourage monitoring and auditing. Each organization must determine which monitors and audits are appropriate to address the requirements of applicable laws, regulations, needs, and available resources. Each organization is also responsible for specifying the method for determining whether the activity is legitimate or suspect and any necessary consequences such as. It mandates audit trails be maintained within the EHR.
Internal audit processes must be in place, and regular system activity reviews must be completed for logins and accessing files. Security incidents must be monitored and resolved. Audit data must continuously be reviewed and analyzed, processes that may also require additional resources. Establishing business rules is very similar to the process historically occurring in the medical record committee, and in medical staff bylaws, rules and regulations. Business rules implement these processes and designate who can document what in the record and how the documents are to be handled.
This business rule presented here should not be considered a complete business rule, nor does it represent all of the business rules needed for an EHR system.
Business rules are specific to an organization and its EHR system configuration. Business rules authorize specific users or groups of users to perform specified actions on documents in particular statuses. Business rules apply to document definition, user class, or user role. You can then add, edit, or delete rules, as appropriate. The grace period for purge has expired and the report text has been removed from the online record to recover disk space.
Note: only completed documents can be purged. The chart copy of the document should be retained for archival purposes. The document has been released or uploaded, but an intervening verification step must be completed before the document is displayed. Business rules are complex, and there are additional rules for inheritance of business rules, inheritance along the document definition line, overriding business rule inheritance, inheritance along the user class line, and inheritance and addenda, etc.
Verifying authorship hinges on two concepts: authentication and access management. Authentication and access management can be executed either through the EHR software, or it can be controlled through a separate, or layered, software application. Access management, also known as authorization, is the process of verifying that a known person has the authority to perform a certain operation.
Verification of the identity of a user or other entity is a prerequisite to allowing access to information systems. The ability of the organization to maintain a legal medical record in the electronic environment is a paramount consideration in the selection and use of an EHR system.
The EHR must have the ability to record all activity that occurs within the system. See Section B. In order to improve patient care and reap further benefit from the problem list as a data resource, the medical community needs to create clear, consistent, complete, and accurate problem lists.
In ethnographic and qualitative studies of healthcare providers, Wright et al. Practitioners have developed their own style on how to manage and organize the problem list [ 5 , 6 ]. For example, practitioners may argue that listing a family history of breast cancer directly on the problem list is important for prompting more frequent testing, another practitioner can debate that its inclusion duplicates the family history section and clutters the list.
Zhou et al. With no common guidelines for how to approach the problem list, issues such as missing problems [ 1 , 2 ] and lengthiness [ 8 ] decrease the potential benefits problem lists could bring to patient care. The nationwide transition to the EHR in the United States [ 13 ] brings the possibility to standardize parts of the medical record in order to improve patient care.
To comply with meaningful use, practitioners must maintain an up-to-date problem list of current and active diagnoses based on ICDCM or SNOMED CT, clinical coding standards designed to classify diseases, symptoms, and other relevant factors about a patient. In addition, at least 80 percent of all unique patients must have at least one entry or an indication of none recorded as structured data in the problem list [ 14 ]. Meaningful use standards are preparing the EHR to enable clinical decision support and population management tools [ 13 , 14 ].
These tools depend on reliable platforms of aggregated data such as the medication list [ 4 ]. Yet, the problem list is not currently supportive enough for these tools due to its inconsistencies, specifically missing problems [ 7 ] and clutter [ 8 ]. The problem list will need more than a common language platform to support these tools [ 10 ].
If the problem list were standardized — i. Further, the problem list is becoming part of the shared medical record across providers and organizations. Specifically, as part of the menu set of meaningful use measures, providers must provide a summary of care record for transitions of care and referrals which must include the problem list [ 14 ]. As a likely seed for common shared medical record, creating a common approach to the problem list will be important to reaping the most value from health information exchanges.
Policies on the problem lists can be found through a range of organizations such as AHIMA which released best practices for problem lists in [ 8 ]. Other organizations with policies related to the problem list include Health Level 7 [ 15 ] and The Joint Commission [ 16 ]. Of course, the federal government also included new requirements for the problem list within the meaningful use standards.
The impact of these policies has not yet been measured and, with the exception of meaningful use, it is not clear that any of the policies have been adopted widely. Based on a review of these current policies, most address what administrators should provide for the problem list with the strongest focus on coding. Guidance for how practitioners should approach, manage, and organize the problem list is largely limited to high level definitions about the problem list.
From the policy perspective, practitioners are left to their own personal judgment for what to include and not include in the problem list. Education and training within healthcare organizations does not appear to provide any further guidance for most practitioners.
According to Wright et al. Some healthcare organizations in the United States created their own policies towards the problem list, but it is unclear how effective they are at producing valuable problem lists nor are they in widespread adoption across the United States.
Therefore, while policies offer high-level rules, specific guidance to the practitioner on how to construct and maintain an accurate problem list is noticeably absent, leaving room for errors and variation in practice. Policies and EHR tools are likely the best approach to solving the issues with current electronic problem list [ 6 ]. Yet, very little research exists on how practitioners make decisions regarding what to include in the problem list and therefore the best common approach to the problem list is unknown.
This knowledge would help the medical community move forward in developing such mechanisms. The purpose of this study is to develop a better understanding of how practitioners think about and use the problem list. A secondary purpose is to study the extent to which practitioners differ in their decision making and if these decisions vary based on practitioner characteristics such as clinical work experience, specialty, and age.
Such research will assist in the pursuit of developing policies and tools that can create a common approach to the problem list. Of note, the EHRs at these facilities allow both coded and free text diagnosis to be entered into the problem list, providing the practitioners with great freedom in how they approach the problem list.
A survey instrument was created to identify practitioner attitudes towards the problem lists in areas that were predicted to be variable. Then the survey instrument was administered through a two-pronged approach.
First, in person interviews were conducted with practitioners to understand the logic behind their hypothesized actions towards the problem list. Second, an online questionnaire was sent to practitioners to gain numerous viewpoints.
For the data analysis stage, both data sets were used in conjunction to create a summarized analysis of practitioner opinions towards the problem list. These steps are outlined in Figure 1 below. Based on the premise that well-meaning practitioners will differ in their views about actions towards the problem list, the survey instrument focused on areas of action towards the problem list that were thought to be highly variable across practitioners.
These non-standardized areas were defined and categorized based on the prior research experiences of the study team. Based on the knowledge learned from these undertakings, the areas of focus for the survey instrument are defined and explained in Table 1.
After determining the specific areas to study, the survey instrument was constructed to gather practitioner opinions. This survey instrument has two sections: the first part asked background questions such as clinical discipline, age, medical experience, and importance of the problem list to the respondent measured via a Likert scale.
The second part of the questionnaire consisted of vignettes. Each vignette contained a hypothesized clinical scenario that covered one of the predefined areas under question along with multiple choice responses of potential actions. For instance, the question of whether family history should be included in the problem list was represented by the following vignette:. Now her sister was recently diagnosed.
To validate that the questionnaire was medically accurate and appropriate, the survey instrument was reviewed by a physician and pre-tested with a focus group. Focus group participants included physicians who were currently students at the Harvard School of Public Health.
The survey was conducted through the Turning Technologies Audience Response System Turning Technologies, Youngstown, OH where each student anonymously responded to a vignette and the results were then displayed on the screen. The focus group leader then prompted a discussion asking individuals to describe their thought process behind their decision. Overall, the focus group respondents understood the vignettes, were engaged in the exercise, and had definite opinions about their answers.
After the validation process, the survey instrument was further refined to ensure questions were clear and reordered to place compelling questions at the beginning to encourage completion of the instrument.
The survey instrument was then implemented via the prior described two-pronged approach: in-person interviews and online questionnaire. A representative sampling method was not feasible and contacted practitioners were selected based on prior participation in a problem list study at the hospitals.
In-person interviews were conducted between December and February During the interviews, the interviewer followed the survey instrument.
When answering the vignettes, the interviewer followed up with more specific questions to learn how the respondent justified their answer. All interviews were recorded for data tracking purposes. No incentives were provided to respondents. The practitioners were unaware of the gift card prior to the interview. For the online questionnaire, the survey instrument was formatted and data collected using REDCap electronic data capture tools [ 18 ].
To address issues that arose during the in-person interviews, the survey instrument was slightly modified for use in the online questionnaire. Overall, it is not believed that the modifications affected the intent of the questions between the in-person interviews and online questionnaire.
The online questionnaire was hosted from March through June Physicians including primary care providers and specialists , physician assistants PAs , and nurse practitioners NPs were included in the sample. The online questionnaires were sent to several departments at BWH and MGH consisting mostly of primary care providers. The respondent pool was determined based on access to department electronic mailing lists which required approval by department directors.
All surveyed departments received approval for participation in the study and only one invitation to a division of specialists received no response and thus did not participate in the study. Data collected through the in-person interviews were partially transcribed, partially summarized where appropriate. The same person who conducted the in-person interviews completed the transcription. In creating Additional file 2 : Appendix B, the transcriptionist CH looked for quotes that represented the different justifications behind decisions towards the problem list in each vignette.
The data were then tabulated to observe the proportion of responses to each answer. These proportions were combined with the quotes taken from the in-person interviews in order to create a general analysis of each area of non-standardized practice discussed in the results section [Additional file 2 : Appendix B].
Using the responses to the online questionnaire, two summary measures were created using the following methodology:. The completeness score displays how many problems the respondent wanted to add to the list across the vignettes. Respondents who answered with the relative majority received one point. Respondents who answered with any other response received zero points.
For vignette question with more than two responses, respondents received one point if they answered with the response that received the highest percentage of respondents the plurality and zero points if they answered with any of response.
An aggregate measure was then calculated for each respondent. All vignette questions were analyzed in this measure unless the question responses were evenly split and no plurality existed. Invitations to complete the online questionnaire were sent out to practitioners. One interview had to be excluded from further analysis as the practitioner did not feel confident in his ability to appropriately answer the vignettes.
The other eight in-person interviews were completed with practitioners across a variety of disciplines and experience levels Tables 3 and 4.
Table 3 describes the characteristics of our online questionnaire respondent base. The distribution of age closely followed the distribution of experience among respondents.
Demographic data sets were not available for non-respondents in departments that agreed to participate or the one department that was invited to participate in the questionnaire, but did not respond to the invitation.
Table 4 includes the demographic data of respondents to the in-person interviews. Experience varied widely and only one respondent was a resident. Additional file 2 : Appendix B includes select quotes from the in-person interviews. The quotes show some of the logic and opinions behind potential answers to the vignettes.
The tabulated data from the online questionnaire are included for convenient reference. Overall, the in-person interviews brought out the complexities behind contentious issues in creating the problem list.
Table 5 includes the tabulated answers to each individual vignette from the online questionnaire. Of the thirteen vignettes with a yes or no response, twelve had a meaningful majority answer statistically significant based on a binomial distribution.
The one question that was evenly divided covered whether hospitalizations should be included in the problem list question4 respectively in Table 5. The following represents a brief summary of the findings and trends in each category as described in the methodology.
This category covers whether content such as family history, social history, surgeries, and hospitalizations should be included in the problem list [Table 5 and Additional file 2 : Appendix B]. The in-person interviews revealed that practitioners want this information to be easily accessible, but it can be in their own separate list in the medical record or categorized within the problem list.
The biggest concern was adding these factors in both locations, creating redundancies across the medical record and extra work for the provider. Some practitioners agreed the family history was important enough to be on the problem list, regardless if it was also included in the family history section of the EHR. One practitioner discussed that she would want to see an occupation listed if it correlated strongly with exposure to a known health hazard.
The category covers more finely detailed specifications for inclusion of problems. From the online questionnaire, practitioners showed that they are not limited to the strictest definition of a problem list; namely, chronic diagnoses. In-person interviews showed that inclusion of these detail specific issues are not as straightforward as the online questionnaire displayed and the action often depends on the patient. For example, in response to question 5 about the woman diagnosed with asthma that was taking no medications and experiencing no symptoms, one practitioner responded that for a young person with few health problems it would not be an issue to add asthma to the problem list.
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