Module 4: Improving and Maintaining the Quality of EMR System Data

Welcome to this module on Improving and Maintaining the Quality of EMR System Data. Data quality plays an essential role in evidence-based decision making. Strong data quality supports strong decision making, leading to an increased demand for data. Poor data quality erodes confidence in the decisions made with such data and reduces the likelihood that decision makers will continue to seek out data to inform their decisions. Read the learning objectives to discover what you will learn in this module.

Learning Objectives

After taking this module, you’ll be able to:

1. Identify key threats to the quality of EMR system data and how they affect the quality of health services
2. Identify appropriate solutions when data quality is threatened
3. Explain how EMR system features promote data quality
4. Check paper and electronic forms for data quality

Learning Activities

• Video: Improving and Maintaining the Quality of EMR System Data (2 min)

  Welcome to this module on improving and maintaining the quality of EMR system data. We will start this module with an example of using an EMR for decision making.

  Instructions: Click or tap the video to play.
• Reading: Data and the Real World (5 min)

  Let’s begin by answering a fundamental question. What is quality?
  Quality is a state of being free from defects or deficiencies. It is the extent to which something has features which are good or bad.

  When we talk about data quality, we refer to the degree to which the data represents reality. The more the data mirrors the actual state of a patient’s or population’s health, the stronger we consider the data. When we talk about poor data quality, we mean that the data does not reflect the real world.

  The quality of the data that is collected, stored, and managed by an health information system―whether it uses an EMR system or not―has an impact on the quality of the data that is reported, analyzed, and used for decision making.

  
• Reading: Aspects of Data Quality (5 min)

  Instructions: To learn more about these aspects, click or tap each button.

  • Accuracy
  • Reliability
  • Completeness
  • Precision
  • Timeliness
  • Integrity

  Accuracy

  Also known as validity. Accurate data are considered correct; the data measure what they are intended to measure. Accurate data minimize error (e.g., recording or interviewer bias, transcription error, sampling error) to a point of being negligible.

  Reliability

  The data generated by a program’s information system are based on protocols and procedures that do not change according to who is using them and when or how often they are used. The data are reliable because they are measured and collected consistently. Example: Duplicate patient records exist because staff create new patient files without fully searching for an existing record, spell names differently, and other data entry practices. As a result of poorly followed practices, the reliability of the patient record is at risk.

  Completeness

  An information system from which the results are obtained is appropriately inclusive. It represents the complete list of eligible persons or units and not just a fraction of the list.

  Precision

  The degree to which repeated measurements under unchanged conditions show the same results. For example, if only the pediatric age field for “age in years” is available rather than “age in months” and / or “age in days” then this can result in measures which are imprecise. A healthcare worker cannot enter an infant’s age as “6 months” or “18 months.” The “age in years” field means that she must enter 6-month or 18-month-old infants as “1 year old.”

  Timeliness

  Data are timely when they are up-to-date (current) and when the information is available on time. Timeliness is affected by:

  • The rate at which the information system is updated
  • The rate of change of actual program activities
  • When the information is actually used or required.

  Integrity

  Data generated by an information system are protected from accidental manipulation or deliberate bias and/or manipulation for political or personal reasons. For example, only those with data management rights have access to the part of the system where patient records can be merged. This prevents others from making changes to a patient’s record. Another example is inflating the daily workload numbers in order to create demand for extra health personnel in a certain department.

• Activity: Data Quality Under Attack (15 min)

  Any aspect of data quality can be compromised. Let's look at what happens in three different scenarios where data quality is at risk. For each scenario, you will first hear about how data is being used by a decision maker. Then, you will be asked to reflect on what might happen as a consequence of using the data available in the situation. Finally, you will have a chance to choose what might have caused the data quality issues.

  Once you have gone through the consequences and causes of one data quality scenario click or tap the “More Causes of Poor Data Quality” to reveal other potential threats to data quality.

  • Checking patients out of visit
  • Counting on CD4 count results
  • Program enrollment
  • More causes of poor data quality

  Checking patients out of visit

  Scenario:

  When a facility uses the EMR system at point-of-care, KenyaEMR records the date and time when patients are checked in for a visit and when they are checked out at the end of a visit. As patients arrive at the CCC each day, they go to Reception, where they are checked in for their visit. At the end of the patient’s clinical visit, some providers will check the patient out. At the end of some days, reception will check out any patients who have not been checked out yet. Some patients remain checked in. They may be checked out several days or weeks later.

  

  Question: When clinic staff check patients out at different times in the CCC's workflow, which of the following do you think will happen? Select all that apply.

  1. Unclear to clinicians which patients have been seen.
  2. The average length of patient visits will be reported as longer than they really are.
  3. Reception will set up a duplicate patient record when the patient comes for a follow up visit.
  4. The medical superintendent will assign too many clinicians to work on the days when the visits are longest.
  5. It will be difficult to know which patients to track as lost to follow up.

  Clear Feedback

  The correct answers are B, D, and E. Visit duration can be calculated from the EMR using the date and time the patient was checked in and the date and time they checked out. For instance, a patient checked in at reception and then checked out by a clinician may have a recorded visit duration of two hours. A patient checked in and out by reception can have a visit duration of up to 8 hours. For patients who are not checked out until days later, their visit duration will reflect the number of days that passed until they were checked out – even when they actually spent three and a half hours at the CCC. This means that an average visit duration calculated based on this data will be longer than the visits really are. Managers using these averages may make a decision to increase staffing in order to bring down the visit duration – when there is no actual need for additional staff.

  ---

  Question: What do you think causes such poor quality data? Select all that apply.

  1. The wrong checkout date and time is entered.
  2. Facility staff did not receive proper training or SOPs on how to check a patient out using KenyaEMR.
  3. Reception assumes that clinicians are checking out patients.
  4. Reception follows instructions to check all patients out at the end of the day.
  5. The patient could not be checked out because the power was out when the patient left the CCC.

  Clear Feedback

  The correct answers are A, B, C, and E. The challenge presented here raises questions about the accuracy, reliability, and precision of the patient’s data affected by this issue, as well as the facility’s data (in aggregate). While the EMR system has a mechanism to close all visits at once, health care workers using KenyaEMR to check patients in and out for visits should follow the protocols about when and who checks patients in and out of the EMR.

  Counting on CD4 count results

  Scenario:

  During a patient’s six month follow up visit, Dr. Onyango orders the same, routine laboratory tests that he has ordered for this HIV positive patient for the last two years. Two weeks later, Dr. Onyango reviews his patient’s laboratory test results in the EMR System and is concerned to learn that the patient’s CD4 count is 170- low enough to consider placing his patient on ART. He begins writing out a prescription for ARVs and up-dating the patient’s chart. In up-dating the chart, his notes remind him of the patients’ clinical presentation, which did not include any symptoms that would suggest such a low CD4 count.
  
  Dr. Onyango goes to the laboratory and asks the laboratory technician to review the results from his patient’s tests. The Laboratory Results Reporting Form that the laboratory technician gives to the clinician lists the patient’s absolute CD4 count as 710, not 170.

  Question: What do you think will happen as a result of the problems with the data? Select all that apply.

  1. Valuable commodities can be wasted when patients are put on treatment before necessary.
  2. Patient will receive treatment when he does not need it.
  3. Motivation to collect and use data, via EMR systems or paper tools, will diminish.
  4. Decisions will be based on inaccurate data or no data at all.

  Clear Feedback

  The correct answers are A, B, C, and D. Any of these options may occur. As decision makers experience more data quality issues, however, they are more and more likely to mistrust the data that they have access to – whether via the paper form or an EMR system. Whether clinicians use poor data or none, the extent to which their decisions are informed by evidence decreases.

  ---

  Question: What do you think could have caused the poor data on CD4 count? Select all that apply.

  1. The system did not save the data in the format that the clerk entered it.
  2. The data clerk transposed numbers while typing them in.
  3. The data clerk put in another patient’s results by mistake.
  4. The data clerk entered the wrong results―on purpose.

  Clear Feedback

  The correct answers are A, B, C, and D. Any of these can cause a data quality issue like the one encountered by Dr. Onyango. Each one affects a different dimension of data quality as well: each calls the accuracy and integrity of the data into question. Whether a person purposefully enters the wrong data or the system is unable to save the data correctly due to a malfunction, the data’s integrity weakens.

  Program enrollment

  Scenario:

  One of Nurse Wanyama’s MCH patients recently gave birth at the maternity. Nurse Wanyama knows from the patient’s EMR record that she is HIV positive and that she had been receiving PMTCT services. She recognizes that the patient’s infant son may have been exposed to HIV and proceeds to set up an EMR record for the child in order to document and link the infant to the right services. Nurse Wanyama enters child’s family history and then enrolls the infant in the HIV program - instead of the MCH program.

  

  Question: What do you think will happen as a result of enrolling the infant in the HIV program instead of the MCH program? Select all that apply.

  1. A “Start on ART” flag will appear in the infant’s EMR record.
  2. A flag for an antibody test at 18 months will appear in the infant’s EMR record.
  3. The HIV exposed infant will start ARTs.
  4. HEI outcomes will not be recorded for the patient.
  5. Growth, nutrition, and development monitoring will be documented.
  6. MCH program reports will have complete and accurate data.

  Clear Feedback

  The correct answers are A, C, and D. When an HIV exposed infant is enrolled in the HIV program instead of the MCH program:

  • A “Start on ART” flag will appear in the HEI patient’s EMR record.
  • A clinician may start the HIV exposed infant on ART.
  • HEI outcomes will not be recorded for the patient.

  Some EMR systems have features that support clinical decision-making. Many of these features are flags or alerts, triggered when specific clinical data is entered into the system. Enrolling an HIV exposed infant into the HIV program instead of the MCH program means users collect different clinical data for the patient. This can result in HIV program alerts, such as “Start ART,” appearing in a patient’s EMR record instead of MCH program alerts. Clinicians following HIV program protocols for patients enrolled in the HIV program may find that they have mistakenly started an HIV exposed infant on ART. MCH program alerts, such as an antibody test flag, won’t appear when the infant is 18 months old because the infant is not enrolled in the MCH program. Forms for recording HEI outcomes will not be available to the clinician, thus these outcomes will not be recorded in the patient’s record. Similarly, 5growth, nutrition, and development monitoring will not be documented.

  Reports that include data on the patient’s HEI outcomes, development monitoring, and DNA PCR testing results are populated with data entered on MCH – Child Services forms. Data for each HIV exposed infant enrolled in the HIV program instead of the 6MCH Program will not be included in these reports, compromising the report’s data quality.

  ---

  Question: What do you think could have caused the poor data on program enrollment? Select all that apply.

  1. The MCH - Child Services Enroll button was not working when Nurse Wanyama clicked on it.
  2. Nurse Wanyama did not notice that she clicked on the wrong program enrollment button.
  3. The receptionist typed in “12” instead of “1” when entering the patient’s age, leading Nurse Wanyama to believe the patient was an adult to be enrolled in the HIV program.
  4. Nurse Wanyama was not trained to enroll HIV exposed infants into the MCH program.

  Clear Feedback

  The correct answers are A, B, C, and D. Poor data quality can be caused by the person using the software or a malfunctioning system. Users do not always realize that they have clicked on a button in error or added an extra number or letter. This can affect the accuracy of the data. When users use the system without knowing the procedures for doing so, such as enrolling HIV exposed infants in the MCH program, then the reliability of the data can become an issue.

  In cases where a part of the system, such as the MCH-Child Services Enroll button, is not working as it is supposed to work, the integrity, accuracy, reliability, and completeness of the data can also be called into question.

  More causes of poor data quality

  These three scenarios brought common threats to data quality to light. More causes of poor data quality may include:

  • Person recording or entering data accidentally transposes numbers
  • Accidentally skip a field and enter data into the next (wrong) field
  • Poor or lack of understanding of how fields are to be filled
  • Workload of person recording or reporting data is so great that they do not gather all of the data
  • Wrong keystrokes
  • No follow up with missing data on registers or records when compiling reports
  • Importance of a particular piece of data is not known or well understood, and therefore not collected
  • Quality of the data collection tools may be affected by frequent changes, insufficient or untimely supply and illegible content
  • Particular pieces of data are assumed to be collected at other points in the process (age, sex, weight, drugs dispensed, lab results)
  • Records or registers do not collect all of the data needed for reporting or decision making
  • The system is not working as expected
  • Poor understanding of medical terminology and or program indicators
  • Handwriting in registers or records is illegible
• Quiz: How EMR Systems can help (5 min)

  Many of the same causes of data quality issues in a paper system arise where an EMR system is also in use. However, the likelihood that some of the same causes will occur is reduced―though not entirely eliminated―by several unique features of an EMR system.

  Instructions: See if you can match the cause of poor data quality with the data quality supports found in an EMR system. Click or tap the drop-down menus to match each cause with the correct data quality support.

  Poor understanding of medical terminology and or program indicators: - select - Data fields skipped because they are assumed to be collected by someone else: - select - Importance of a piece of data not known or understood and so not collected: - select - No follow-up with missing data when compiling reports: - select -

  Clear Feedback Undo selection

  feedback_text

• Reading: Improving and Maintaining Data Quality (5 min)

  Once we know the probable causes of poor data quality, it is easier to assess, improve, and maintain data quality. Formal processes have been designed to detect existing data quality problems, remedy them, and prevent new problems with data quality from developing: data quality assurance and data quality control.

  What, when, and how data quality is assessed usually indicates whether national or program-specific data quality assurance or data quality control procedures are being followed. For instance, formal data quality assurance activities, such as data quality audits, take place periodically (quarterly, semi-annually, annually, etc.). They typically involve assessing data at multiple sites and administrative levels in order to identify system-related issues or problems. These activities use a set of data quality indicators for each aspect of data quality as well as specific data quality assessment or audit tools to measure the quality of data.

  Facilities may also have their own plan or practices for monitoring and maintaining data quality on a daily, weekly, monthly, or quarterly basis.

• Reading: When is Data Quality Assessed? (5 min)

  Data quality can be assessed at any three points in time: before, during, or after data collection. Some assessments will look at different aspects of data quality during these periods. For instance, a form’s design can be assessed before data is collected to determine if it supports the reliable collection of correct data.

  Instructions: Click or tap each button to see examples of what can be assessed at each point in the timeline.

  • Before
  • During
  • After

  Before

  Form design, staff training on data collection and data quality

  During

  Check data entry for completeness and accuracy

  After

  Gauge the completeness, timeliness, and accuracy of collected data

• Activity: Taking Action to Improve Data Quality (15 min)

  While most of the assessment methods you just learned about are used in formal data quality assurance and data quality control activities, users still play an active role in assuring data quality. Let’s return to our three data quality scenarios and see what options are available to staff to address the data quality issues that you identified earlier.

  Instructions: Click or tap each button to see what action the data clerks and clinicians can take in response to the data quality threats they encountered earlier.

  • Checking patients out of visits
  • Counting on CD4 Count Results
  • Program Enrollment

  Checking patients out of visits

  Question: What are some ways to address this problem? Select all that apply.

  1. The receptionist or provider can double check the date and time they enter to ensure that it is correct.
  2. Arrange a CME session where staff can review the process for checking out patients using KenyaEMR.
  3. This problem is routine and doesn’t need to be addressed.
  4. The facility in-charge should disregard visit durations reported as longer than one day.
  5. Distribute job aids on how to check patients out.
  6. Call a staff meeting to clarify roles and responsibilities for checking patients out.
  7. Check to see that the UPS and generator are running properly.

  Possible Causes Clear Feedback

  When clinic staff check patients out at different times in the CCC’s workflow:

  • The average duration of a patient visit will be reported as longer than visits really are.
  • The average duration of a patient visit will be reported as longer than visits really are.
  • These patients will be at greater risk of loss to follow up.

  The causes are:

  • The wrong checkout date and time is entered.
  • The clinician did not receive proper training or SOPs on how to check a patient out using KenyaEMR.
  • Reception assumes that clinicians are checking out patients.
  • The patient could not be checked out because the power was out when the patient left the CCC.

  The correct answers are B, E, and F. When staff are not consistently trained in or following the same procedures, the data’s reliability is threatened. To improve the reliability of patient checkout data, CME sessions, distributing job aids, and clarifying roles and responsibilities are all actions that facility management can take to support staff to follow the same data entry processes and procedures assigned to them. Disregarding data in results is not the best solution to data quality issues. Making such selections introduces bias into decision making and can lead to a decision based more on intuition than actual evidence.

  Counting on CD4 Count Results

  Question: What are some ways the software can be improved to address these problems? Select all that apply.

  1. Test the software to find and fix problems saving data before installing it at a facility
  2. Check data entry before saving the form
  3. Check that the correct patient record is open before entering data
  4. Have the system import the lab results directly into the EMR system
  5. This problem is routine and doesn’t need to be addressed
  6. Routinely (daily, weekly) check EMR system data against paper forms
  7. The data manager should correct the results in the EMR system

  Possible Causes Clear Feedback

  Problems:

  • Valuable commodities can be wasted when patients are put on treatment before necessary.
  • Patient will receive treatment when he does not need it.
  • Motivation to collect and use data, via EMR systems or paper tools, will diminish.
  • Decisions will be based on inaccurate data or none at all.

  The causes are:

  • The system did not save the data in the format that the clerk entered it.
  • The data clerk transposed numbers while typing them in.
  • The data clerk put in another patient’s results by mistake.
  • The data clerk entered the wrong results―on purpose.

  The correct answers are A and D. Before software is installed or updated at a facility, it is tested to make sure it works properly. If the problem is caused by a software bug, then users should report it to the support team so that action can be taken by testers and developers. While importing lab results directly into the EMR system will not fix any data quality problems, it can reduce the likelihood of data entry mistakes. Users can make data entry mistakes; however, the system cannot fix them. Depending on when the data entry error is identified, the user or the data manager will need to address the problem.

  Program Enrollment

  Question: What are some ways to address these problems? Select all that apply.

  1. Test the software to find and fix problems with buttons that do not work.
  2. Check data entry before saving the form.
  3. This problem is routine and doesn't need to be addressed.
  4. Check the form header before filling the form and make sure you clicked on the correct form.
  5. Make sure all staff receive training or attend CMEs on patient enrollment for different programs.
  6. Routinely (daily, weekly) check EMR system data against paper forms.
  7. Enroll the patient in the MCH program and discontinue HIV program enrollment.

  Possible Causes Clear Feedback

  Problems:

  • When an HIV exposed infant is enrolled in the HIV program instead of the MCH program.
  • A “Start on ART” flag will appear in the HEI patient’s EMR record.
  • A clinician may start the HIV exposed infant on ART.
  • HEI outcomes will not be recorded in the patient’s EMR record.

  The causes are:

  • The MCH - Child Services Enroll button was not working when Nurse Wanyama clicked on it.
  • Nurse Wanyama did not notice that she clicked on the wrong program enrollment button.
  • The receptionist typed in “12” instead of “1” when entering the patient’s age.
  • Nurse Wanyama was not trained to enroll HIV exposed infants into the MCH program.

  The correct answers are B, D, E, and G. Entering data incorrectly, such as transposing numbers or using the wrong field, are common data entry errors that users can easily prevent or correct. Users should routinely check data, either before saving an entry or at the end of the day, week, or month. Once a data entry error such as this has been made, the user should enroll the patient in the correct program and alert the data manager to the incorrect data for further action.

• Reading: What users can do to improve data quality (5 min)

  Remember, you don’t have to wait until the MOH team arrives at your facility for a data quality audit to do something about the quality of the data in your EMR system’s database. Any of these actions will increase the likelihood that data in the database will reflect reality.

  • Check data entry before saving the form
  • Check that the correct patient record is open before entering data
  • Flag paper forms with missing or inaccurate data
  • Alert providers or others to complete or correct data when errors are detected
  • Collect data using established and consistent protocols and procedures
  • Respect time schedules for data entry and reporting as indicated in your facility’s SOPs
  • Give feedback and discuss data quality during review meetings, routine staff meetings and CMEs
  • Make user manuals and SOPs accessible to staff using the EMR system
• Reading: What data managers can do to improve data quality (5 min)

  HRIOs and others who manage data quality can take additional actions to correct and improve data. Once data has been incorrectly entered into the system, responsibility for correcting the error no longer lies with the user. This is taken up by data managers. In addition to the actions taken by users, those who manage data quality may:

  • Create a quality assurance plan
  • Visually inspect completed forms
  • Run EMR system data quality reports to identify missing data
  • Make data quality reports available to users
  • Routinely (daily, weekly) check EMR system data against a random selection of paper forms
  • Correct inaccurate data and fill in incomplete data detected
  • Control user correction of data errors
  • Give feedback of data quality results and recommendations
  • Resolve identified causes of data errors, such as merging duplicate patient records
  • Implement identified system changes
  • Communicate with users

  
• Reading: Data Quality and EMR Systems (5 min)

  Good quality data for decision making is the result of good data entry, data quality control, and assurance mechanisms. In addition to the actions users can take to monitor and maintain data quality, EMR systems have built in features that are designed to support the entry of quality of EMR system data.

  Notice that these features either guide users to enter quality data or enable users to identify at-risk data. EMR systems do not magically prevent or fix data quality issues. The responsibility for ensuring data quality remains with users and those responsible for assuring and controlling data quality.

• Reading: Where Does Responsibility Lie for Data Quality? (5 min)

  Instructions: Click or tap each button to learn what responsibilities lie with the EMR user or the EMR system.

  • EMR System Users
  • EMR System

  EMR System Users

  • Check data entry before saving the form
  • Check that the correct patient record is open before entering data
  • Routinely (daily, weekly) check EMR system data against a random selection of paper forms
  • Run EMR system data quality reports to identify missing data
  • Flag paper forms with missing or inaccurate data
  • Collect data using established and consistent protocols and procedures
  • Respect time schedules for data entry and reporting
  • Learn to use the EMR system and follow data quality procedures

  EMR System

  • Use menus or checkboxes to standardize responses
  • Use standardized date format, ranges, calculations
  • Use data concepts from a standard data dictionary
  • Use alerts to flag required fields to ensure completeness
  • Prevent duplication of patient records by using unique patient identifiers
  • Compile data quality reports to identify key data missing from records
• Activity: You make the call (15 min)

  Instructions: Let’s see how well you can spot problems with data quality. Click or tap each button to learn more about these data quality issues.

  • Mixed up patient files
  • Missing data
  • Entering data retrospectively
  • Case Scenarios Conclusion

  Mixed up patient files

  Scenario:

  You are the data manager at Dangazeli District Hospital. Your current aim is to reduce the number of duplicate records in KenyaEMR. Each week, you review possible duplicate records and merge those that belong to the same patient. During your review, you come across these two patient records. Take a close look: do these two records belong to the same patient?

  

  Question: Patient 1 and Patient 2 are the same patient.

  1. True
  2. False

  Clear Feedback

  The correct answer is True.

  ---

  Question: How do you know these are the same patient?

  1. Same birthdate, name, and gender―but the Patient Clinic Number contains transposed numbers.
  2. The records were created on the same date.
  3. Patient 1 has two given names - one of which is shared with Patient 2.

  Clear Feedback

  The correct answer is A.

  • For Choice B: these records may belong to the same patient, even if they were created on different days.
  • For Choice C: Sharing a given and last name is not uncommon. You will want to verify that additional data in the patients’ records are duplicated before concluding that they are the same patient name.

  As with the other scenarios you have seen, duplicate records have their own causes. These introduce data quality issues that can lead to serious consequences.

  Causes:

  • Failure to locate existing patient record
  • Use of different spelling when entering names
  • Entering a second name as first name
  • Date of birth is given at one visit but not another

  Consequences:

  • Patient’s record incomplete
  • Reliability of patient’s data is weakened
  • Laboratory results may be recorded in one record while the clinician views the duplicate record
  • Clinicians will make decisions based on the incomplete records
  • Patients may not receive the care they need when they need it

  Missing data

  Scenario:

  Tomorrow is the weekly staff meeting and you want to give feedback on how well staff are picking up KenyaEMR and entering complete data. You check the total number of patients in the database (121) and run Patients Missing Entry Point and Patients Missing date confirmed HIV+ reports. Less than 10% of patients are missing either piece of data.

  

  Question: What do these two reports tell you about the current quality of the data in KenyaEMR?

  1. Data completeness is satisfactory
  2. Data completeness is unsatisfactory
  3. Data accuracy is unsatisfactory
  4. The data in the reports are not sufficient

  Clear Feedback

  The correct answer is A. It is a good practice to have as complete data as possible. These come close―and there is room for improvement. For additional information about the other choices:

  • If you selected data completeness is unsatisfactory: Data can have a very small amount of missing data and still be of good quality.
  • If you selected data accuracy is unsatisfactory: How would you determine data accuracy using these reports?
  • If you selected the data in the reports are not sufficient: These reports may not give data on all dimensions of data quality, however, you use them to make some decisions about data quality.

  Entering data retrospectively

  Instructions: Test your knowledge with 2 questions about entering data retrospectively

  • Question 1
  • Question 2

  Question 1

  Can you find the four data entry errors on this MOH257?

  Click or tap on the data fields to find the data entry mistakes.

  

  Reason

  The facility where the patient was confirmed HIV positive is missing. The patient record is incomplete.

  ×

  Regimen

  This family member is HIV positive; the In Care and CCC Number fields should be filled. The patient's family history is incomplete.

  ×

  Who

  The date the patient is enrolled in HIV care is missing.

  ×

  Height

  The patient's ART history has not been filled. This results in an incomplete patient record.

  ×

  Try again

  Please try another spot on the form.

  ×

  Question 2

  Can you find the four data entry errors on the EMR system’s triage form?

  Click or tap the data fields to flag the data entry mistakes.

  

  Date

  This data is not entered in a timely manner. The form is being filled on 28 November when the patient was checked in on the 26 November.

  ×

  BP

  This data is not complete since the clinician did not fill both fields.

  ×

  Respiratory Rate

  This data could be inaccurate if the digits were transposed.

  ×

  Vital Signs

  The vital signs did not change from the signs recorded at the patient’s January visit. If the provider copied these signs instead of recording the patient’s actual vital signs, then the data’s integrity is at risk.

  ×

  Try again

  Please try another spot on the form.

  ×

  Case Scenarios Conclusion

  Let’s look at the different dimensions of data quality that can be compromised on the MOH257 or in the EMR system’s triage form. Both forms can have problems with accuracy, completeness, and precision. What about the other dimensions?

  Both forms can have any number of fields with missing data. You can see here on the MOH257 that these include the date the patient was enrolled in the HIV program and where. On the EMR system’s triage form the blood pressure was not completely filled.

  Here you can see that the MOH257 form included a birth date with an illegible birth year. One of the advantages of an EMR system is that entries are always legible. While the precision of the data recorded on the triage form may not be clearly at risk, free text fields where entries can lack adequate detail and precision present a risk.

  The MOH257 does not indicate when the form was filled, making it difficult to know if it was completed in a timely manner.

  In many cases, it is difficult to know from looking at the forms what data entry errors are the result of staff receiving poor training or the lack of processes and procedures to follow. If staff were not aware what fields must always be filled, for instance, then the data’s reliability will weaken over time.

  Both forms can have problems with the integrity of their data. An incomplete patient clinic number on the MOH257 could mean that the wrong data is being recorded on the wrong record. And here, the vital signs entered on the EMR’s triage form appear to be copied from a previous visit, meaning that they may not reflect the patient’s current condition.

• Reading: Summary (5 min)

  When clinicians and managers have access to quality data, they can be confident in the decisions they make using that data. The scenarios that you have worked with in this session shows that poor data quality can have serious consequences, including failure to provide patients with the care and treatment that they need. You have also learned what causes different data quality problems and the actions you can take to improve data quality.

  EMR systems can make data management easier in many ways. EMR systems have some features to support data quality, such as alerts, reports, and patient record merging. Remember that EMR systems cannot always prevent data entry errors or other data quality issues and that EMR systems do not always detect or fix such problems either. Responsibility for data quality lies with the people using the EMR system: receptionists, nurses, clinicians, data clerks, and managers. Now go on to the final quiz.

• Quiz: Post-Test (15 min)

  After you take the module, please be sure to take the post-test. Read each question and select the correct answer. Please note that the post-test will open in a new website page. You will need access to the Internet to complete the post-test.

  Click to go to the post-test Post-test Module 4