Module 3: Using EMR Data for Decision Making

Welcome to this module on Using EMR Data for Decision Making. The purpose of this module is to make quality data available and easily accessible to those making a variety of decisions. How can an EMR system make it easier for you to use data to make stronger decisions about patient care, program or resource management, or policy? 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. Describe the process of transforming data into information used for decision-making
2. Explain the importance of evidence-based decision making to improve service delivery
3. Explain how an EMR system supports evidence-based decision making
4. Identify KenyaEMR reports that can inform specific service delivery, resource management, and programmatic decision making

Learning Activities

• Video: Using Data Is Easier With EMRs (5 min)

  Welcome to this module on using EMR data for decision making. We will start this module with an example of using an EMR for decision making.

  Instructions: Click or tap the video to play.

  After you watch the video, click the button below the video to dissect the example.

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  Decision Making

  Let’s take a closer look at the decisions made by Dr. Mutende, Dr. Ochieng, and NASCOP. Their decisions and those made by other health workers aim to bring about a number of improvements and efficiencies, such as those described here:

  Decisions made by health care workers aim to:

  • Improve the health status of the country’s population
  • Improve quality of health services
  • Allocate resources effectively
  • Monitor progress towards national objectives or targets
  • Strengthen the health system
• Reading: Who Makes Decisions and Where? (5 min)

  At all levels of the health system, decisions are made that impact the care and treatment that patients receive. Decisions made at the national and county level about policy, program design and direction, and resource management have facility level implications. Administrators and clinicians at facilities routinely make decisions about facility resources, activities, and clinical care and treatment.

  Instructions: Click or tap the buttons to see examples of decisions made at each level and who makes them.

  • Facility
  • County and Sub-County
  • National

  Decision makers: medical superintendent, facility-in-charge, clinicians

  • Clinical
  • Resource Management
  • Programmatic

  Clinical care

  • Diagnoses
  • Type of follow up
  • Starting treatments
  • Selecting appropriate treatments
  • Changing treatment regimens due to side effects
  • Viewing results from selected lab tests

  Resource Management

  • Equipment management
  • Stock management
  • Staff scheduling
  • Vehicle scheduling
  • Budgeting

  Programmatic

  • Community outreach activities
  • Peer educator programs
  • Mentoring programs
  • Service availability

  Decision makers: county health management teams, County Health Director

  • Clinical
  • Resource Management
  • Programmatic

  Clinical care

  Adherence to guidelines among facilities within the county.

  Resource Management

  • Equipment management
  • Stock management
  • Staff hiring
  • Posting and scheduling
  • Vehicle scheduling
  • Budgeting

  Programmatic may include:

  • Community outreach activities
  • Peer educator programs
  • Mentoring programs
  • Service availability

  Decision makers: MOH, NASCOP, NPHLS, WHO, PEPFAR, etc

  • Clinical
  • Resource Management
  • Programmatic
  • Policy

  Clinical care

  Influences clinical practice at the facility level, resource assignment and management at the county and facility level.

  Resource Management

  • Commodity procurement
  • Staff profiles
  • Staff posting
  • Budgeting

  Programmatic

  • Design, implementation, and scale up of program activities at the national level
  • National level media campaigns, population-wide incentive programs, etc.

  Policy

  • Developing or updating standard treatment guidelines
  • Essential medicines lists
  • Dispensing protocols
  • Standardizing or updating data collection tools
• Reading: What Influences Decision Making? (10 min)

  All of these decisions are influenced by different factors, ranging from actual data to hearsay. Evidence-based decision making occurs when decisions are informed by data, transparently and together with stakeholder or patient consultation. Some flags help clinicians identify patients who are ready to start an HIV regimen based on their CD4 count or WHO staging while others may indicate missed appointments or test results.

  Decisions that are influenced by stories, powerful or influential connections, politics, or at random are examples of intuition-based decision making. This means that the process of making decisions is solely based on something that is known, perceived, understood, or believed by instinct, feelings, or nature - without actual evidence.

  Instructions: Click or tap the buttons to learn more about intuition-based decision making.

  • Anecdotes
  • Connections
  • Political
  • Arbitrariness

  Anecdotes

  Stories or anecdotes about other patient’s experiences with various care or treatment options are used to inform decisions about a patient. For instance, if Dr. Ochieng heard from a fellow staff member that a particular patient had been rumored to have secretly sold ART drugs obtained at another facility, she may be reluctant to enroll this patient on treatment when there are so many others she can start on treatment.

  Powerful & Influential Connections

  Third parties who wield power and influence may use them or be perceived to use them to influence care and treatment decisions. If Dr. Ochieng knows that a patient’s brother is a well-known member of a group of merchants, this information may lead her to enroll the patient in the HIV program and set her up to start treatment in order to make a favorable impression on the brother.

  Political

  Personally held political views may introduce bias when a policy-maker, administrator, or clinician makes a decision. For example, if Dr. Ochieng learned that a patient is heavily involved in promoting a political agenda that goes against Dr. Ochieng’s political beliefs, she may pay less and less attention to this patient’s progress on treatment over time.

  Arbitrariness

  Decisions made on an ad-hoc basis, without consistency. Screening random patients for TB―whenever the provider remembers to do so and regardless of any clinical observations or information―is an example of an arbitrary decision.

• Quiz: Evidence or Intuition? (10 min)

  What does an evidence-based or intuition-based decision look like? Read 2 examples and decide if they are based on evidence or intuition.

  Example 1:

  It seems that more women are coming to the clinic seeking ANC services. I suspect this is because few people know about family planning. I will talk to the community team about doing more outreach on family planning and the contraceptives available at the clinic for next month.

  Question: Is this decision based on intuition or evidence?

  1. Intuition
  2. Evidence
  3. Neither

  Clear Feedback

  The correct answer is A. This decision is based on intuition. No data shows that family planning knowledge is low―there is only the suspicion that this is the cause of the increase in patients.

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  Example 2:

  From the monthly ANC report, it looks like 15 more women came in for ANC services this month. The stock status report indicates that we have not had a stock out of contraceptives in several months. I remember at our last meeting, the community team talked about an ANC promotional campaign that was piloted nearby in late September. Now that I look at the records of some of these new patients, it looks like they are coming from this area. This new campaign seems to be working! I’ll see what I can do to expand it to other areas.

  Question: Is this decision based on intuition or evidence?

  1. Intuition
  2. Evidence
  3. Neither

  Clear Feedback

  The correct answer is B. This decision is based on the evidence from two reports, patient records, and information provided by the community team.

• Reading: Strengthening Decisions by Using Data (5 min)

  Intuition based decision making tends to capture the social context in which decisions are made--and only calls on clinical or managerial experience and expertise. When made in the absence of data, this approach to decision making can lead to overlooking routine events, over-estimating rare ones, and introducing errors and biases. More importantly, the decision you make based on our instinct does not always address the actual problem.

  Using data to inform decisions addresses many of these limitations. When you use data to make decisions, you have greater confidence that you are addressing the actual problem with a solution that is more likely to work. Additionally, evidence based decision making enables others to see and understand why decisions were made, holds decision-makers accountable, and accurately monitors progress towards performance targets.

  
• Reading: Using EMR System Data to Make Decisions (5 min)

  Using data for decision making relies on the availability of and access to data. EMR systems are powerful tools that make quality data easily accessible to those who want to use the data for decision making.

  Unlike paper records that are stored in on and off site filing rooms, EMR systems make retrieving, organizing, and aggregating patient data relatively easy. Most EMR systems allow users to run reports, access or create graphs, or export data that can be further manipulated in a different program, such as Excel.

• Activity: Scenario – Treating HIV Patients (15 min)

  Let’s take a closer look at how EMR data can be used to make the different types of decisions that we were talking about earlier. You will work through 4 different decision areas, beginning with treating HIV patients.

  Instructions: Click or tap the buttons below to work through the scenario. Please move left to right to successfully complete this activity.

  • Scenario
  • Starting Treatment
  • Running Reports
  • Conclusion

  Scenario

  Dr. Ambrose Otieno is the CCC in-charge at Majimazuri Health Center. His goal for the upcoming quarter is to have 80% of eligible HIV patients started on treatment. In July, he uses KenyaEMR to prepare the monthly MOH711 report. In reviewing this report, Dr. Otieno notices that 25 patients started on ARVs this month. The same report tells him that 200 patients are eligible to be started on ARVs. He is aware that KenyaEMR usually raises a yellow alert or flag indicating when a patient is eligible for ART. What data will help Dr. Otieno decide how to increase the number of HIV patients on treatment?

  

  Goal: 80% of eligible patients started on treatment
  MOH711:

  • 25 patients started on ARVs this month
  • 200 patients eligible for ARVs

  Question: What data will help Dr. Otieno make a good decision about starting more eligible HIV patients on treatment this month?

  1. Names of HIV patients who are lost to follow up
  2. The names of patients eligible for ART and the names of patients who are lost to follow up
  3. The number of patients whose partners’ HIV status is unknown
  4. The names of MCH patients who have not been tested for HIV
  5. The number of patients enrolled in the HIV program

  Clear Feedback

  The correct answer is B. These two pieces of data can help Dr. Otieno be very specific about the patients to follow up with. He can advise the community team to trace ART-eligible patients who are lost to follow up, while also working with the clinic staff to enroll ART-eligible active patients at their next visit.

  To learn more about why the other options are not the correct answer, read the following information

  • Option A: On its own, these data have limited use for Dr. Otieno in increasing ART enrollment among eligible patients. The list of names would probably include patients who have not yet met ART eligibility criteria, patients who are ART eligible, patients who have already started ART, and those who have moved away or died. This will make it difficult to know which patients to target for ART enrollment.
  • Option C or D: This data has limited use for Dr. Otieno: it does not tell him who should start treatment.
  • Option E: This data has limited use for Dr. Otieno: it will tell him how many patients are already enrolled in the HIV program and he still will not know who should start treatment.

  Starting Eligible HIV Patients on Treatment

  Now that you have an idea about the data that Dr. Otieno can use to increase the number of eligible patients on ART, what EMR reports will he need to generate in order to have that data?

  Read about the data provided on each report.

  • MOH711: National Integrated From for Reproductive Health, HIV/AIDS, Malaria, TB, and Child Nutrition. Contains aggregate data for any specified month.
  • MOH731: NASCOP’s comprehensive HIV/AIDS facility reporting form, containing aggregate data for any specified month.
  • Patients with declining CD4: Total number of patients, male and female who have declining CD4 counts, hyperlinked patient name, age, sex, Unique Patient Number, previous CD4 count and current CD4 count.
  • Patients with known HIV status at enrollment: Total number of patients, male and female, whose HIV status is known at enrollment in MCH program, hyperlinked patient name, age, and sex.
  • Patients who are lost to follow up: Total number of patients, male and female, who are lost to follow up, hyperlinked patient name, age, sex, and Unique Patient Number.
  • Patient dashboard: A banner at the top of each patient’s medical record where core patient data is displayed (age, date of birth, sex, patient/registration numbers, etc.) as well as their visit status (out-patient, current, etc.). Based on specific criteria, different yellow alerts will be displayed here as well. These alerts include: Due for CD4, Missing TB Sputum Results, Eligible for ART, On ART, etc.
  • Patients eligible for ART: Total number of patients, male and female who are eligible for ART, hyperlinked patient name, age, sex, and Unique Patient Number.

  Starting Eligible HIV Patients on Treatment

  Question: What reports can Dr. Otieno use to help him meet his goal?

  Instructions: Click or tap each option to find out which reports help Dr. Otieno to reach his goal.

  Option A Run a Patients who are lost to follow up report

  Run a Patients who are lost to follow up report

  Reviewing the Patients who are lost to follow up report, Dr. Otieno suspects that some of the patients listed on this report are also eligible for ART, but he does not know which ones to follow up with as the MOH711 does not list out their names.

  On its own, this report does little to help Dr. Otieno reach his goal. What other reports do you think Dr. Otieno should try running next?

  Option B Run a Patients eligible for ART report

  Run a Patients eligible for ART report

  Dr. Otieno now knows which patients are eligible for ART. He reviewed this report with other clinicians to stress the need to start these patients on treatment at their next visit. Some of these patients have appointments scheduled for the next month; clinicians start those patients listed on the report on ART.

  A month later, Dr. Otieno runs the Patients eligible for ART report again and compares it to the previous report: 60 of the 200 names are no longer listed. The new MOH711 report for the month indicated a similar change in the number of patients who started ART.

  This is a good use of this report. Can Dr. Otieno make a greater improvement if he uses other or more reports?

  Option C Remind clinicians to review patient records for an Eligible for ART alert in a patient’s KenyaEMR dashboard

  Using the Eligible for ART Alert

  Dr. Otieno speaks with the other clinicians and shows them how KenyaEMR displays a yellow alert when patients become eligible for ART. As patients come for their follow up visits, clinicians decide to initiate many of those with a yellow alert on treatment.

  The next month, Dr. Otieno sees a small increase in the number of patients started on ART and around 100 of the names that were on last month’s Eligible for ART report are no longer listed on this month’s report.

  

  To find out if this was the right report to use, click or tap the Conclusion button at the top of this activity.

  Option D Run a Patients Who Are Lost to Follow Up and Patients Eligible for ART reports

  Run Patients Who Are Lost to Follow Up and Patients Eligible for ART reports

  With the Eligible for ART alert, Dr. Otieno is confident that the facility’s clinicians will be able to identify active patients, eligible for ART, as they come in for their scheduled visits. At the next staff meeting, he shares this information with his team and shows them when and where the alerts appear. At the same time, he provides the Patients Lost to Follow Up report to the community team so that they can target these patients for outreach.

  Throughout the month, the other clinicians use the alerts to initiate treatment for eligible patients when they come in for their follow up visit. In the meantime, Dr. Otieno worked with the community team to reach out to those who are lost to follow up.

  A month later, he prepared the MOH 711 and ran a report on Patients lost to follow up. The names on the Lost to follow up report have gone down, but Dr. Otieno does not know if any of them have started ART. The MOH 711 shows a small increase in the number of patients starting ART.

  

  Did Dr. Otieno use the right reports to inform these decisions? Click or tap the Conclusion button at the top of this activity to find out.

  Option E All of the above

  All of the above

  With the Patients lost to follow up report and the Patients eligible for ART report, Dr. Otieno is able to identify those patients who are both eligible for ART and lost to follow up. At the next meeting, he shares this information with his team of clinicians. Dr. Otieno also demonstrates how KenyaEMR displays an alert on a patient’s record when they are Eligible for ART or Due for CD4.

  Throughout the month, the other clinicians use the alerts to initiate treatment for eligible patients when they come in for their follow up visit. In the meantime, Dr. Otieno worked with the community team to follow up with those who are eligible to start ARVs and lost to follow up.

  A month later, he prepared the MOH711 and ran reports on Patients lost to follow up and Patients eligible for ART. Many of the names are no longer on the Lost to follow up report, 130 of the 200 names are no longer listed as Eligible for ART. The MOH711 is also reporting a similar increase in the number of patients starting ART.

  Did Dr. Otieno use the right reports to inform these decisions?

  These reports are excellent choices―when used together!

  Using the data from these three reports as well as the Eligible for ART alerts allowed Dr. Otieno to make decisions about who to target and how. He also used these reports and the MOH711 to monitor progress towards his goal of starting 80% of eligible patients on treatment.

• Activity: Scenario – Improving MCH Patient Care (15 min)

  You are doing a great job so far! Let’s look at another scenario on how EMR data can be used to make the different types of decisions that we were talking about earlier.

  Scenario

  Dr. Susan Nkirote, the MCH in-charge at Kolol Health Centre, hopes to better integrate PMTCT services into the care and treatment she gives HIV positive MCH patients. She decides to look more closely at her patients’ KenyaEMR Charts during their follow up visits and use data from their charts to determine when PMTCT services can be initiated. At Mary Nzioka’s visit, Dr. Nkirote calls up Mary’s chart.

  

  Instructions: Click or tap on all of the data displayed on Mary Nzioka’s chart that Dr. Nkirote can use to guide her decisions.

  

  On ART

  Yes! This data tells us that Mary is following an ART regimen.

  ×

  Family History

  Yes! This data tells us that Mary and her spouse are discordant couples. Dr. Nkirote can also see that three of Mary’s family members do not know their HIV status.

  ×

  HIV

  Yes! This data tells us that Mary is currently enrolled in the HIV program.

  ×

  MCH Mother Services

  Yes! This data tells us that Mary is currently enrolled in the MCH program.

  ×

  ART start date

  Yes! This data tells us when Mary started ARV treatment and the regimens that she has followed.

  ×

  Try again

  Please try another spot on the form.

  ×

  ---

  Question: Which decisions can Susan Nkirote make based on the data on Mary Nzioka’s chart? Select all that apply.

  1. Refer Mary to the TB Unit
  2. Link Mary to PMTCT services
  3. Provide Prevention with Positives messaging
  4. Initiate Mary on a new ART regimen
  5. Test Mary for HIV

  Clear Feedback

  The correct answers are B, C, and D. Although Mary is already on ARV treatment, she can receive other PMTCT services, such as counselling on infant feeding. Mary’s family history indicates that she and her partner are discordant—and none of her other children have been tested for HIV. This is an opportunity to encourage disclosure, HIV testing, and provide psychosocial support to the family. Mary should be following the recommended first-line regimen for pregnant women. A substitution or switch in regimens will be needed.

• Activity: Scenario – Strengthening Integration of Family Planning and Prevention With Positives (15 min)

  Next, you will complete a third case scenario that focuses on strengthening integration of family planning and prevention with positives. Begin by reading the following scenarios and then responding to a couple questions.

  Instructions: Click or tap the buttons below to work through the scenario. Please move left to right to successfully complete this activity.

  • Scenario – Part 1
  • Scenario – Part 2

  Scenario – Part 1

  Latum Mission Hospital wants to make sure that the right patients receive consistent messages about family planning and prevention with positives.

  

  Question: What three pieces of data can Latum Mission Hospital’s health care workers use to strengthen integration of family planning and prevention with positives?

  Instructions: Click or tap on 3 of the choices and see how the data is used to make a decision, then click the Feedback button.

  1. Names of patients whose HIV status is known
  2. Number of patients using modern contraceptives
  3. Names of MCH patients in their third trimester
  4. Number of patients provided with condoms
  5. Names of patients on treatment

  Clear Feedback

  Good choice! Health workers can use the names of patients who are HIV positive to target their intervention - and then use the number of patients using modern contraceptives and provided with condoms to determine the effectiveness of their efforts.

  Good choice! Knowing which MCH patients have a known HIV status at enrolment and the names of patients on treatment will help health workers target their follow up on family planning and prevention with positives. They can use the number of patients provided with condoms to monitor if the targeted messaging yields results.

  Good choice! Health workers can use the names of patients who are HIV positive, the names of MCH patients in their third trimester, and those on treatment to target these groups of patients.

  Almost! Data on which MCH patients are HIV and how many of their MCH patients are using modern contraceptives can help the health care workers target their family planning messaging and determine if their intervention is affecting the number of patients using modern contraceptives. Knowing the names of MCH patients in their third trimester, however, will not strengthen their decision. Try again!

  Almost! Data on which MCH patients are HIV and how many of their MCH patients are using modern contraceptives can help the health care workers target their family planning messaging and determine if their intervention is affecting the number of patients using modern contraceptives. However, they may not see the results they seek if they only focus on patients who are already on treatment. Try again!

  Not quite! Health workers can use the data on patients using modern contraceptives and patients provided with condoms to determine how well their efforts to integrate MCH and HIV services are working. However, they may not see the results they seek if they only focus their messaging on those MCH patients in their third trimester.

  Try again! Knowing how many patients are using modern contraceptives will help track if increased messaging on family planning is effective. However, using the names of MCH patients in their third trimester or on treatment to target these groups of patients is unlikely to achieve the kinds of results they seek.

  Try again! Knowing how many patients are who have been given condoms will help track if increased messaging on prevention with positives is effective. However, using the names of MCH patients in their third trimester or on treatment to target these groups of patients is unlikely to achieve significant results.

  Almost! Data on which MCH patients in their third trimester are HIV positive can help the health care workers target their family planning messaging. Health care workers can also track the number of patients provided with condoms to determine if their intervention is affecting the number of patients leaving the facility with condoms. However, they may not see the results they seek if they only focus their messaging on those MCH patients in their third trimester.

  Not quite! Knowing how many patients are using modern contraceptives or who have been given condoms will help track if increased messaging on family planning and prevention with positives is effective. Also, knowing the names of patients on treatment will enable health workers to target this group―although they will not be able to target HIV positive MCH patients who have not yet started treatment.

  Scenario – Part 2

  Health care workers at Latum Mission Hospital decided to use data on the number of patients using contraceptives, the number of patients given condoms, and the names of MCH patients who are HIV positive.

  Question: What reports should Latum Mission Hospital’s health care workers use to strengthen integration of family planning and prevention with positives?

  Option A Run an MOH 731 report

  Run an MOH 731 Report

  Sorry, on its own, this report does not help the team identify specific patients to target for counselling. Try again!

  Option B Run an MOH 711 report

  Run an MOH 711 Report

  Sorry, this report does not help the team identify patients to target - or monitor their efforts to integrate family planning and prevention with positives messaging into MCH services. Try again!

  Option C Generate a list of patients with known HIV status at enrolment and run and MOH 731 report

  Generate a list of patients with known HIV status at enrolment and run and MOH 731 report

  Excellent choice! Based on this data, the team decides that all women of child bearing age, particularly those listed in the Patients with known HIV status at enrolment report, must be counselled on family planning and prevention with positives messages during every visit. They also agree to update the patient’s KenyaEMR record to reflect the counselling activities that occur. Using the MOH 731, they can also monitor their progress from month-to-month.

  Option D Generate a list of patients with known HIV status at enrolment and run and MOH 711 report

  Generate a list of patients with known HIV status at enrolment and run and MOH 711 report

  Try again. While the Patients with known HIV status at enrolment report will help the team focus their family planning and prevention with positives messaging, the MOH 711 report will not help them monitor the effectiveness of their efforts.

  Option E Run an MOH 731 Report and an MOH 711 Report

  Run an MOH 731 Report and an MOH 711 Report

  Sorry, the team can use the MOH 731 to monitor the changes due to their counselling, however, neither report helps them identify specific patients to target for counselling. Try again!

• Activity: Scenario – Improving National TB Policies (15 min)

  Keep up the great work. Let’s complete one more case scenario focused on improving National TB Policies.

  Instructions: Click or tap the buttons below to work through the scenario. Please move left to right to successfully complete this activity.

  • Scenario
  • Reports

  Scenario

  The National Care and Treatment Technical Working Group is holding their quarterly meeting this month. Their agenda includes an examination of co-infection trends and discussion of recommended policy changes.

  

  Question: What data should they use to inform their discussion? (select all that apply)

  1. Total number of HIV patients testing positive for TB
  2. Total number of TB patients with a positive HIV test result
  3. Total number of HIV patients screened for TB
  4. Total number of enrolments in TB program
  5. Total number of enrolments in HIV program

  Clear Feedback

  Options A, B, and C are all good choices. This data can help the TWG understand current trends. The following information tells you why options D and E are not as good of choices.

  • Option D: On its own, the total number of enrolments in TB program will not give the Technical Working Group a complete picture of the number of patients enrolled in the TB program who are also HIV positive.
  • Option E: On its own, the total number of enrolments in HIV program will not give the Technical Working Group a complete picture of the number of patients enrolled in the HIV program who are also diagnosed with TB.

  Reports

  The Technical Working Group has decided to review data from the last six months on the total number of

  • HIV patients testing positive for TB
  • TB patients with a positive HIV test result
  • HIV patients screened for TB

  Question: What reports should they use to inform their discussion?

  Instructions: Click or tap each report to reveal additional information.

  Review MOH 711

  Review MOH 711

  The MOH 711 data, aggregated to reflect facilities throughout the country, gives the Technical Working Group a strong sense of the spread of TB among HIV positive patients. The Technical Working Group recommends that the National TB and Leprosy Program adopts and promotes the “5 I’s of Managing and Controlling TB in Co-Infected Patients.”

  

  Want to see what other recommendations could be made? Click or tap the other buttons.

  Review MOH 731

  Review MOH 731

  The aggregated MOH 731 data shows the Technical Working Group members that TB screening of HIV patients is lower than anticipated. After much discussion, the Technical Working Group advises the National TB and Leprosy Program to encourage facilities to take up a stronger focus on intensive case finding, the 5thI of managing and controlling TB in co-infected patients.

  

  Want to see what other recommendations could be made? Click or tap the other buttons.

  Review both MOH 711 and MOH 731

  Review both MOH 711 and MOH 731

  The aggregated data from both reports suggest that increased TB screening is leading to an increase in the number of HIV positive patients who are co-infected. This leads the Technical Working Group and the National TB and Leprosy Program to institute the 5 I’s of managing and controlling TB in co-infected patients.

  

  Want to see what other recommendations could be made? Click or tap the other buttons.

• Reading: How We Use Data When Making Decisions (5 min)

  As you have seen from the different decisions made by Dr. Otieno, Dr. Nkirote, health care workers at Latum Mission Hospital, and the National Care and Treatment Technical Working Group, data plays an essential role in making evidence based decisions. Data helped each of these decision makers as they engaged in a process of identifying a problem, determining possible solutions, and selecting the best way forward. Quality data, when available, improves our ability to make effective decisions.

  Instructions: Click or tap each step in the decision making process to see how and when data can help make strong decisions.

  • 1
  • 2
  • 3
  • 4
  • 5

  Identify the problem

  Use data to understand what the problem actually is, who is involved, and where and when it occurs.

  Determine possible courses of action

  Use data to identify different outcomes of possible solutions.

  How has this problem been resolved in the past?

  Decide on a single course of action

  Data can be used to assess the different possible outcomes or what the solution would require.

  Implement a single course of action

  Depending on the solution you arrive at, seeking out additional data may be a part of putting your decision into place.

  Monitor and evaluate the outcome

  Subsequent data on the problem can indicate whether the solution you implemented has been successful.

• Reading: Making Data Useful (5 min)

  Let’s take a closer look at what happens to data during this process. In each decision-making scenario, the decision makers started with raw data and turned it into useful information. A single piece of data, for instance, a CD4 count of 600, is more useful when we compare it with other data, such ranges for laboratory test results or clinical observations. As soon as we have data that we can compare, data becomes information that we can analyze and use to make decisions.

  
• Reading: The Process (5 min)

  Instructions: Click or tap each stage in the diagram to find out more about this process.

  

  Data

  Simple measures of characteristics of people and things has little inherent meaning or value on its own. Knowing that 15 ANC visits occurred this month is not useful for decision making―until other data or information is available.

  ×

  Information

  Data that are processed to be useful provides answers to “who,” “what,” “where,” and “when” questions. Knowing that 4 out of the 15 ANC visits were made by patients testing positive for HIV makes this data more valuable for decision making.

  ×

  Knowledge

  Application of data and information. Clinicians and facility-in-charges can make clinical or programmatic decisions that can lead to enrollment of HIV+ ANC clients in PMTCT services or starting a patient on prophylaxis.

  ×
• Reading: Summary (5 min)

  You’ve just learned about how evidence-based decision making improves service delivery by pinpointing issues and developing targeted responses.

  You also learned how to turn data into useful information by comparing a single piece of data with other data, making it analyzable information. Through the examples provided, you now know how an EMR system can generate reports that clinicians, policy-makers, and managers use to support evidence-based decision making.

  Remember, MOH reports can be useful in identifying trends that inform policy decisions or monitor progress towards goals. HIV, MCH, or TB reports can provide more specific data about patients that clinicians and facility managers will find particularly useful in informing service delivery and resource management. 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 3