How To Maintain Data Discipline in P&P

How To Maintain Data Discipline in P&P Maintaining Data Discipline in P&P The discussion below provides information on how to develop data standards and how to clean and maintain data in your EMR so you can manage your patients as a population. Utilizing data standards ensures that the queries you run in your EMR are accurate and can help you manage the recall of patients who need to be followed on a proactive basis. Data standards are the choices we make about how we will name particular diseases (e.g., diabetes, NIDDM, DM type 2, etc), what values we will capture for particular lab tests (e.g., 7% or 0.07 for HbA1c) or what names we use for capturing medications (e.g., ramipril or Altace). Data cleaning is the process by which data in your EMR is converted from a non-standard term or value to a standardized term. For example changing all instances of DM to Diabetes with an associated ICD-9 code of 250.00 Data discipline is the system and processes you have in place to ensure that all health providers and office staff are mindful to utilize data standards during daily use of the EMR, that your office processes are attuned to the use of data standards and that your EMR is properly set up to help you maintain those standards. For example, once you have cleansed your data, data discipline would be the process of making sure that all the patients that suffer from Diabetes have an ICD-9 code of 250.00 associated with them, or that all the patients with an ICD-9 code of 250.00 actually have Diabetes. Why use data standards and data discipline? Adhering to data standards and maintaining data discipline allows a clinician to: 1. Feel confident that the data in their EMR can be extracted for population based care. When data is incorrectly entered, the wrong patients end up being identified as having a disease and patients who do have the disease are incorrectly left off the list. 2. Not be bothered with the wrong patients being on a disease list each time a query of the database is repeated. To use CIS for population management requires a bit of discipline and adherence to standards. Once you understand how to maintain data discipline in CIS, it is straight-forward and can be managed by the providers in the practice. In order to maintain your data it is imperative to place the information you record in the correct module of P&P. The table below provides a brief over view of where information regarding a patient should be stored. 2011 Hamilton Family Health Team. All Rights Reserved. Version 1.0 KBL SAP 1 of 5

Information To Be Recorded Immunization Information Tracking Chronic Diseases Maintaining a health concern Section of P&P Immunization Module Chronic Disease Module Health Maintenance Plans NOTE: For more information on any of the topics mentioned in the table below please refer to the companion best practices on How to Document Immunizations in P&P, How to Create a Chronic Disease Module (CDM) in P&P and lastly Managing Health Maintenance Plans in P&P. P&P stores a significant amount of structured data for query purposes. By utilizing the structured data entry features, we can later query the system for that information. Ensuring that your data is clean and well maintained will mean that when you create a query, it will be accurate and ready to use. Medical queries can also be used to help facilitate the process of data cleansing. Recording data in an EMR needs to satisfy medico-legal requirements (audit trail, tamperproof records) as well as information management requirements. Sometimes, these two requirements are in conflict with each other. For example, a patient may have accidentally been diagnosed as having diabetes. For medico-legal reasons that diagnosis needs to remain on the chart. However, for information management purposes, that diagnosis needs to be ignored or discarded. There are two approaches to recording data in P&P, each with it s pros and cons for balancing the medico-legal requirements and information management requirements. Data entry approach Audit log is on Medico-legally Sound Good for Information Management All notes are signed YES NO Audit log is on, Notes are not signed YES YES 2011 Hamilton Family Health Team. All Rights Reserved. Version 1.0 KBL SAP 2 of 5

This document describes 2 ways of capturing data in P&P that allow you to meet medico-legal requirements and meet your information management needs. The first mentioned in the table above has a great risk associated with it. P&P s audit log is known to slow the system down significantly when running for prolonged periods of time or when there are many users on the system. The best way to document your information for medico-legal purposes and maintain information is described below. It is based on a model of signing all notes (for medico-legal soundness) but includes a workaround that allows you to manage information in your system in a reliable manner. Option 1: P&P has a very strict way of capturing data in the EMR. For example, once you have entered data into a SOAP note, saved and signed it, that data cannot be removed or corrected. This is acceptable for medico-legal purposes, but makes information management in the EMR challenging. In this option, you always sign your SOAP notes. In order to capture chronic diagnoses in a structured manner and manage information appropriately, you may want to consider using a single unsigned Progress Note to capture all structured diagnoses, risk factors and other CPP related data. In the Reason for Visit field enter Patient Summary IM (for Information Management) to indicate that this is not a note, but a patient summary. Do not sign that Patient Summary IM Progress Note. By doing so you will be able to add and remove diagnoses and other CPP information easily. The ability to add and remove diagnoses, risk factors and other important information is a very valuable capability that allows you to ensure that data in the EMR is clean for information management purposes. Diagnoses should be placed in the IM Progress note, using the ICD-9 Diagnosis. For example, if a patient has Diabetes Mellitus, use the ICD9 code of 250. Refer to the best practice document on how to update a diagnosis. NOTE: Since P&P stores data in signed SOAP notes permanently, please ensure that the diagnosis is confirmed before entering it into the SOAP note. Since you won t be at risk for mislabeling patients inappropriately, you can now save your SOAP notes to be medico-legally compliant while being information management friendly. Option 2: In this option, you must have the audit trail on and leave your notes unsigned. That way, if you find dirty data in the system, you can easily change it, since it is not locked. Because you have the audit trail on, any changes you make to your record will be logged in the audit trail and thus your charting will be medico-legally sound. 2011 Hamilton Family Health Team. All Rights Reserved. Version 1.0 KBL SAP 3 of 5

There is one caveat to this approach: the audit trail is quite difficult to read and may not be acceptable to a medico-legal body, such as the College of Physicians and Surgeons of Ontario. This is an area that has not been explored and there are few cases that could illuminate this area. Risk factors such as smoking and alcohol should be recorded carefully. The proper way to record changes in smoking or alcohol (or other risk factors) is to change an existing record, if one exists. For more information on managing risk factors please refer to the best practice on Managing Risk Factors. P&P has a relatively good approach to structuring data, but it has to be carefully managed to get the maximum benefit from it. Over time, the data in an EMR gets dirty. There are several types of dirty data: Clinicians may inadvertently put an incorrect diagnosis into the record. E.g., recording diabetes in the encounter note when a patient is only suspected of having diabetes. In P&P, these patients will show up on all diabetes related queries. 1) Data is inconsistently entered into the system. E.g., Patients with Insulin or HbA1c >0.07 don t have a diagnosis of Diabetes Type 2 or Type 1 in the Problem List. Dirty data is inevitable in an EMR system. You will need to run data clean-up queries in your system on a regular basis (i.e., bi-annually to annually) to make sure that data quality is high. In order to clean your data it is important to first develop a query for all patients that have that diagnosis. For more information on how to develop a query please refer to the best practice on how to create a simple query. The next step is to go through all the records that result from that query and ensure that the patients do suffer from that condition. For example, for patients suffering from diabetes, you would want to ensure that all the patients in that query are accurately diagnosed. Option 1 (signed notes): you will need to add an additional coded diagnosis into the Patient Summary IM progress note that indicates that the patient does not have this disease. You can easily do this by creating a pseudo code for non-diabetic patients. While in the Registration Module, click on the Modules menu and select Codes and Dictionaries. Click on Diagnostic Codes. It will bring up a window that will allow you to enter your own diagnostic codes. Create the following: put H01 in the ICD-9 field and put Not Diabetic in the Description Field. Adding a non-diabetic code to those patients who are not diabetic allows you to exclude them in the queries if they have inadvertently been labeled as diabetic. This applies to other diseases as well. 2011 Hamilton Family Health Team. All Rights Reserved. Version 1.0 KBL SAP 4 of 5

Option 2 (Audit trail on, unsigned notes): In this scenario, since you have not signed any notes, you should be able to delete any diagnoses that are incorrect. The best way of doing so is to go to the CPP and finding the incorrect diagnosis in the Problem List. Click on that item and you should be able to go directly to the note I which it is written. Delete the diagnosis. If you d like a record of what you wrote to be visible, just enter the diagnosis in text. Autolists Another way of cleaning data is to clean up autolists. Autolists are lists of terms that are created when you enter data into a field. Autolists are a good way of ensuring that the data you enter is immediately captured into a list that can be viewed later. This allows you to query for terms easily and quickly. The problem with autolists is that they can get cluttered with dirty data very quickly if not properly managed. You can clean up autolists in P&P by going to the Setup menu and selecting Predefined Values Setup. Most autolists are listed there and can be edited easily. Unfortunately, Risk Factors are not available as a Predefined Values list. Once you have cleaned up the data for all your patients, you will need to call the P&P Helpdesk to clean up the Risk Factor autolist for you. You should ask them to delete all terms in the Risk Factor autolist and to add the correct terms instead. The P&P Medical Records Query Module doesn t automatically exclude medications which are stopped from appearing in queries. So for example, patients who had warfarin five months ago, but stopped taking it two months ago, will still appear on a warfarin patient list, even though they are not taking it. To exclude a patient who did have a medication in the last few months, but who stopped taking it, you will need to add the word stopped into the Adverse Reaction field of that medication. You can do this by clicking in the A column in the medication list in the Prescription Writer module. When you get the window requesting a description of the adverse reaction, enter stopped (IM). (The IM stands for information management, indicating that you are using this field for a special purpose.) Now, you will be able to exclude patients whose medication has been stopped from showing up in the queries. You can do this by creating another query in the Drugs/Medications area that excludes (Not) those medications which have an Adverse Reaction of Stopped (IM). The reason we use the Adverse Reaction field is because P&P only allows you to exclude patients with a particular medication on the basis of Effectiveness and Adverse Reaction. Since Effectiveness is a numerical scale with no external validity, using it could get confusing quickly. Using Adverse Reaction is a reasonable compromise. 2011 Hamilton Family Health Team. All Rights Reserved. Version 1.0 KBL SAP 5 of 5