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Tag: Effective Use of EMRs

  • EMR Queries for D2D – Diabetes Care Composite Indicator

    The queries below were developed by QIDSS and the EMR Communities of Practice. They will help you extract data to monitor diabetes care, and they can also help you prepare the data for submission to D2D.

     

    Why Diabetes?

    There are two reasons to include a diabetes care indicator in D2D. The main reason is to include clinical data in primary care performance measurement, especially considering clinical care remains the core business of AFHTO members. Another reason is the need to show the value of team-based care. EMRs are the only repository of data created and used by teams and therefore are the best source of data to reflect the contribution of the entire team to patient care. As well, EMRs are the most up-to-date source of data about the whole person available in primary care.

    A composite indicator

    Following the lead of the EMRALD project, the 4 measures used in the diabetes numerator (HbA1C testing done at appropriate interval and appropriate levels of most recent HbA1C, blood pressure, and cardiovascular protection via statin therapy) will be combined into a single composite indicator for diabetic performance. If you want to start monitoring your diabetes care we have a process to help you. This involves running a standard query to identify your diabetic population and cleaning your EMR by coding those patients accordingly. Check out the suggestions on the “Getting started on a diabetes registry” webpage or contact improve@afhto.ca for more information. Please refer to the EMR-specific instructions below to generate data for the Diabetes Care indicator. Once you have tried running these queries, consider sharing your challenges and successes with your EMR CoP or contact us so we can all get better at doing this!

    Telus PS Accuro Nightingale OSCAR P&P

     

    NOTE: All queries are tested and validated prior to release. However, changes that take place after the queries are released may affect how accurate they are. Such changes could include EMR software updates, new medications, and changes to standard clinical definitions. They may result in false positives, that is, patients being flagged who do not have the specified condition. They may also result in false negatives, that is, patients not being flagged who do have the condition. Queries are also limited by the quality of your EMR data. Please exercise judgement when using them, as they are meant to support and complement a chart review, not to replace it.

    Telus PS 

    CODED: If your diabetic patients are coded with ICD-9, ICD-10, or SNOMED CT – use the D2D Diabetes Care v1 searches (.srx files) to generate data for the diabetes composite indicator. They run quickly! Screenshots of the numerator searches and denominator search can be found in the guide. UNCODED: If you are not sure how your team codes patients with diabetes in your EMR, please use the D2D Diabetes Care uncoded v1 searches (.srx files). They will extract data for all patients identified with diabetes, based on the case definition developed by CPCSSN and translated for EMR use by the QIDSS Algorithm Project. Beware, these comprehensive searches may take a while to run! Screenshots of the numerator searches and denominator search can be found in the guide. Save the searches to your desktop and import them into your EMR. After running the searches, take the list of unique patient ids resulting from each of the 3 numerator searches and the denominator search. Plug these numbers into the Diabetes Care Calculator to generate the composite score for your team. This score is the number to be entered into the D2D submission platform. You might need the help of your QIDSS, IT staff or other person who usually runs queries in your EMR. Consider sharing your challenges and successes with the Telus PS CoP or contact us for more information.

    Accuro 

    CODED: If your patients with diabetes are coded with ICD-9 250 use the 4 queries (D2D- Diabetes Care v1) located on publisher to generate data for the diabetes composite indicator. Once the queries are run, import the patient lists (PHNs) into the Diabetes Care Calculator. You may need the help of your QIDSS, IT staff or any other person who usually runs queries in your EMR. Consider sharing your challenges and with the Accuro CoP or contact us for more information.

    Nightingale 

    CODED: If your patients with diabetes are coded with ICD-9 250 please use the queries illustrated in the links below to generate data for the diabetes composite indicator. Please review this guide before proceeding – it contains instructions and options for calculating your diabetes care score. If you choose to use the Diabetes Care Calculator there are a number of steps involved but it will calculate the diabetes score for you.

    You may need the help of your QIDSS, IT staff or any other person who usually runs queries in your EMR. Contact us for more information.

    OSCAR 

    CODED: Please review this guide before proceeding – it contains screenshots of the reports. If your patients with diabetes are coded with ICD-9 250 – please use the D2D Diabetes Care v2 queries to generate data for the diabetes composite indicator. Two sets of queries were created as a result of query run time issues. The following set of queries search for diabetic patients for all physicians. If you experience excessive query run time you can use the queries that run for each physician. Once the queries are run, import the list of unique patient id’s from each report into the Diabetes Care Calculator. Although a number of steps are involved, it will calculate the diabetes score for you. Consider sharing your challenges and successes with the OSCAR CoP or contact us for more information.

    P&P 

    NOTE: we are unable to create a query for blood pressure due to global data restrictions. The vendor expects a fix to be deployed with the next release. We will post a query as soon as this issue is resolved. We do have 3 other queries (HBA1c testing, HBA1c levels, and Statin) that you can run to start building the composite indicator, as below. Please download the D2D Diabetes Care v1 queries for the diabetes indicator. The numerator queries are based on patients being coded using ENCODE-FM (ICD9  = 250). The denominator query is based on the CPCSSN case definition and will help you identify patients with diabetes who might not be coded in your EMR. Due to the way the lab results are handled in P&P, the report generated by the numerator queries will need to be exported to excel and filtered to find most recent A1c values and dates. Please see screenshots in this guide for reference. Once the queries are run, import the patient lists (unique IDs only) into the Diabetes Care Calculator. Although a number of steps are involved, it will calculate the diabetes score for you. You might need the help of your QIDSS, IT staff or other person who usually runs queries in your EMR to import and execute this query. Please consider sharing your challenges and successes with the P&P CoP or contact us for more information.  

  • EMR queries for D2D – EMR Data Quality: Smoking Status Complete

    Please find below EMR queries developed by QIDSS and the EMR Communities of Practice that will help you extract data for submission to D2D.

    Telus PS Accuro Nightingale OSCAR P&P

     

    NOTE: All queries are tested and validated prior to release. However, changes that take place after the queries are released may affect how accurate they are. Such changes could include EMR software updates, new medications, and changes to standard clinical definitions. They may result in false positives, that is, patients being flagged who do not have the specified condition. They may also result in false negatives, that is, patients not being flagged who do have the condition. Queries are also limited by the quality of your EMR data.

      Once you have tried running these queries consider sharing your challenges and success stories with your EMR CoP or with us so that others can benefit from improved and shared solutions!

    Telus PS (Note – There are issues with queries developed in previous versions of PS. We are in the process of updating the queries and will be available at the launch of D2D (August 21, 2017)

    The D2D EMR Data Quality Smoking Status v1.1.1 searches (.srx files) will extract data for the numerator and denominator for all patients (>=12 yrs) who have smoking status documented in the risk factors module. Save these searches to your desktop and import into your EMR. You might need the help of your QIDSS, IT staff or other person who usually run queries in your EMR. Instructions on how to import the searches into your EMR can be found in this guide. Share you challenges and successes with the Telus PS CoP or contact us for more information.

    Accuro 

    Please find the D2D EMR Data Quality Smoking Status v1 numerator and denominator queries on Publisher. Query criteria and instructions on how to generate rate data for the smoking status complete measure can be found in this guide. You might need the help of your QIDSS, IT staff or any other person who usually run queries in your EMR. Share you challenges and successes with the Accuro CoP or contact us for more information.

    Nightingale 

    Instructions on how to build and run a query in Data Miner to generate data for the smoking status complete measure can be found in this guide. Contact us to share you challenges and successes.

    OSCAR 

    Download the D2D EMR Data Quality Smoking Status v1 query to your computer and import into your EMR. You might need the help of your QIDSS, IT staff or any other person who usually run queries in your EMR. Share you challenges and successes with the OSCAR CoP or contact us for more information.

    P&P 

    The P&P Smoking Status Query v1 file (.dat file) includes the numerator and denominator queries that will help you generate data for all patients >=12 yrs old with smoking status documented in Risk Factors. The data field used in the numerator query is a “learning field” and may need to be customized depending on how your team documents smoking status. You might need the help of your QIDSS, IT staff or other person who usually run queries in your EMR to import and run this query. Share your challenges and successes with the P&P CoP or contact us for more information.

  • EMR queries for D2D – Patients served

    The EMR queries below were developed by QIDSS and the EMR Communities of Practice to help you prepare data for D2D submission.

    Telus PS Accuro Nightingale OSCAR P&P

     

    NOTE: All queries are tested and validated prior to release. However, changes that take place after the queries are released may affect how accurate they are. Such changes could include EMR software updates, new medications, and changes to standard clinical definitions. They may result in false positives, that is, patients being flagged who do not have the specified condition. They may also result in false negatives, that is, patients not being flagged who do have the condition. Queries are also limited by the quality of your EMR data. 

      This indicator is intended to reflect the ENTIRE patient population served by a team, not just those who are rostered to the team. The definition is “the number of unique patients with a visit (i.e. appointment) to anyone in the team in the last 3 years”. This definition will continue to evolve in subsequent iterations of D2D as EMRs are increasingly capable of recording other meaningful patient encounters (e.g. phone calls) in a way that the data can easily be extracted. For D2D 4.0 the technical limitations of data extraction from EMRs dictate that only in-person encounters can be included in the definition.

    Telus PS 

    The D2D- Patients Served v1 search looks at unique patients with an appointment in the last 3 years. Save the search to your desktop and import into your EMR. You might need the help of your QIDSS, IT staff or other person who usually runs queries in your EMR. Consider sharing your challenges and successes with the Telus PS CoP or contact us for more information.

    Accuro 

    Please download the D2D- Patients Served v1 query from the publisher. The query returns patients with an appointment in the last 3 years and filters out “no shows”. You may need the help of your QIDSS, IT staff or any other person who usually runs queries in your EMR. Consider sharing your challenges and successes with the Accuro CoP or contact us for more information.

    Nightingale 

    Please use this guide to extract data for the patients served indicator using data miner. If you have any questions or would like training on data miner, contact us for more information.

    OSCAR 

    Please save the D2D- Patients Served v1 query to your computer. Here is a guide for importing the query and using the report generated by the query. Consider sharing your challenges and successes running this query with the OSCAR CoP or contact us for more information.

    P&P 

    Thanks to efforts of the CoP an approach to accessing appointment data (i.e “date last seen”) has been programmed by the vendor. It’s called the Patient Utilization Report. This guide will show you how to access the report in the EMR. Please connect with the P&P CoP or contact us for more information.

  • Getting started with a registry for patients with depression

    Get even better at tracking how well your patients with depression are doing.

    You can use the EMR tools shown below to get even better at tracking how well your patients with depression are doing.

    Why do we need to do better?

    Depression affects about 5% of adults in Canada each year and double that (i.e., 12%) at some point in their lives[i].  ALL Canadians are affected by depression, even they don’t have the disease themselves. This is because depression costs the Canadian economy at least $32.3 billion each year[ii]. In spite of this, people still have real fears of what people around them (families, friends, people at work, etc.) would think of them if they asked for help with depression. This keeps many people from asking for this help. This is where primary care teams come in. They have long term relationships with patients and can work with them to find the best ways to help them with their health.

    What can we do to get better?

    We can start by making sure we know which patients have depression. If you have a list of all patients that have depression, it will be easier to make sure all the right people are invited to the programs, with less risk of people falling through the cracks. The search tools below can help you find which patients in your EMR are likely to have depression.

    What if I already know which patients have depression?

    You might not need to use this tool if you already have a good list of patients who have depression in your EMR. The search is meant for teams that do not yet have a list of patients with depression and do not have a way to check the records of all their patients to come up with such a list.

    How good is this search tool?

    The search tool was based on the case definition from CPCSSN and the input from experts in depression at Hamilton FHT and St Michaels’ Hospital. The tool has been tested with the help of the eHealth Centre of Excellence EMR environment. The search gives few false negatives but does give some false positives. For every 100 patients that the search finds in your EMR, 62 patients will actually have depression but 38 might not. That means you will have to check the list of patients found in your EMR to be sure that they really do have depression. For a team with 10000 patients, you would likely have 500 patients to review. This is better than looking at all 10,000 patients – or not looking at any at all.

    Which EMRs does the search work on?

    Searches are available for TELUS PS, OSCAR, and Accuro EMRs.

    How much data cleaning do I need to do first?

    You do not need to clean your EMR data before you use the tool.  The testing was done on EMR data as they are right now, for better or worse, so you can be sure there is a good chance it will help you too.   You can just load it into your EMR and run it – ie it is plug-and-play. [i] 5% of Canadians 15 years or over affected by depression any given year.  12% of Canadians affected by depression over their lifetime.  Statistics Canada’s 2012 CCHS. [ii] The Conference Board of Canada: Annual costs of depression due to lost productivity.

    Technical details of the Query Criteria

    (click image to see larger version) depression-case-definition-20161027 The Depression query is intended for teams that do not yet have a reliable list of patients with depression and don’t have the time or resources to start from scratch in reviewing all their patients to generate such a list. Right now, it is also only for teams with PSS or Accuro although work is continuing to expand the standardized query to OSCAR and Nightingale. The following steps will help your team use the query to generate a list of CHF patients, starting from your EMR.

    Step 1. Estimate how many patients you think this will affect.  Multiply the number of patients your team serves by 0.05 (the average rate of depression in Ontario) to get a rough idea of how many of your patients likely have depression. If you still think this is a big enough group of patients for you to generate a registry for, carry on to step 2.

    Step 2. Import the query into your EMR.  Right now, you can only do this if you have either Telus PSS or QHR Technologies Accuro EMRs. You will likely need the help of your QIDSS, IT staff or other person who usually works with your EMR to do this.

    • For PSS, import the PSS SRX file into your EMR
      • This guide provides instructions on how to import the searches into your EMR.
      • Screenshots of the query can be found here. 
    • For Accuro, download the query “AFHTO Depression Frontend Search”from their publisher.
      • This document provides the query case definition information 
      • Here you can find a guide on how to download the query
    • For OSCAR, please click here to download the numerator and denominator queries 

    Step 3.  Run the query in your EMR. Again, you might need the help of your QIDSS, IT staff or other person who usually runs queries in your EMR. Running the query will produce a list of patients with depression. The list will not be perfect – probably 38% of the patients identified by the query will NOT have depression. The query gets you STARTED in building the depression registry but doesn’t do the whole job for you.

    Step 4. Find the patients who might not have depression. Review the list of patients generated by the query to separate out those patients that are clearly already coded as having depression. What’s left will the list of patients who MIGHT have depression based on other data in the EMR besides formal coding.

    Step 5. Prepare your physicians to review the list  Subdivide the list of possible patients with depression into separate, shorter lists for each physician. Work with your physicians to find out if they would prefer a list on paper or electronically and how they might like it sorted (i.e. by name or most recent visit or some other parameter).

    Step 6.  Invite each physician to review their list of patients.  They know their patients best and can likely quickly confirm which ones do or do not have depression, even though that information might not be easy for others to find in the EMR.

    Step 7.  Clean up your EMR data.  Add depression codes to the EMR for each patient that the physician confirms as having depression. This so-called “data cleaning” work is a great job for a student.  AFHTO has created a toolkit to assist members in recruiting and using students for data clean-up. Click here for the toolkit.

    Step 8.  Re-run the query. After you have corrected the EMR, re-run the query to generate a list of patients with depression. This is your new depression patient registry. Going forward, you can run the query anytime you need to generate a list of patients with depression.  You can use the list to invite patients to a depression program, track progress with outcomes on these patients or any other purpose.

    Step 9: Recruit patients to your depression programs.  We will soon be posting resources in setting up a care program for patients with depression.

    Step 10: Measure progress with patient prognosis, management, and overall care. Here are some example outcome measures:

    • % of patients who show an improvement in PHQ-9 score.
    • % of patients who show improvement on CES-D.
    • % of patients hospitalized.
    • % of patients with action plans.
    • % of patients self-identifying as satisfied after a group session.

    This query was produced by and for QIDSS with assistance from eHealth Centre of Excellence in support of all AFHTO members. If you have any questions, please contact improve@afhto.ca.  

  • Exploring Health Equity

    Health equity is achieved when people are able to reach their full health potential and receive high-quality care that is fair and appropriate to them and their needs, no matter where they live, what they have or who they are. AFHTO is committed to helping members work towards this goal and to identify gaps and opportunities to improve health care for all of their patients. We have begun work towards establishing a baseline on social demographic data collection and the capacity of EMRs to record and track this information, starting with a conversation in our weekly QIDSS calls and EMR CoP meetings focused on two main questions:

    • What social demographic data is currently being captured in the EMR?
    • What social demographic data can be easily recorded in the EMR today?

    The Tri-Hospital + TPH Health Equity Data Collection Research Project have begun work on answering the question of what Socio-Demographic factors to measure. Their findings, presented in the report We Ask Because We Care, were reviewed and shared with the QIDSS and EMR CoPs, and we have started our conversation with the data elements presented there. This work will lead to recommendations for adding social demographic data to the EMR Data Quality Indicator for D2D 4.0.

  • Hire a Student: Funding & Placement Programs

    Canada Summer Jobs 2016: Deadline Extended

    The deadline to apply for funding for a summer student through the Canada Summer Jobs Program has been extended to March 11, 2016.

    Private, public, and not-for-profit employers are eligible for this funding. Not-for profit employers can receive funding for up to 100% of minimum wage; public and private sector employers can receive funding for up to 50% of minimum wage. Additional funding is available to cover the cost of accommodating students with disabilities in the workplace. The job must provide meaningful work experience for the student, be full-time (3o-40 hours per week), and have a duration of 6-16 weeks. Students employed through this program are between 15 and 30 years old, were full-time students in the previous academic year, and intend to return to full-time studies in the next one. They must be legally entitled to work in Canada  — this includes Canadian citizens, permanent residents, and persons with refugee status; foreign students are ineligible. For more information and a step-by-step guide to the application procedure, visit the Canada Summer Jobs page at Service Canada. AFHTO members have had success hiring students for a number of different projects including clean-up of EMR data.  A number of them have gone on to work for our members permanently, including some of our QIDS Specialists!

    Related documents

     

    [original post & updates for D2D 3.0]

    Need help getting ready for D2D 3.0? Consider hiring a student!

    You will be able to submit data for D2D 3.0 from December 3 until January 15.   And you might want some help to get ready for that. Students can be a big resource for teams.  If you think you could use someone for nearly a week in Dec (14-18), please contact Barb Nayler with the Health Information Management program at St. Lawrence College.  Even though St. Lawrence is in Kingston, students are available across the province, especially in Toronto and Ottawa.  Several of our fabulous QIDSS are health information management professionals from this or similar programs, so there is a really good chance these students have the right skills to be helpful. If you think you need someone longer than 4 days, there are other options. Read on, and follow the links for more! You’ll find links to lists of student placement programs, provincial and federal government incentives, and guidelines on how to recruit, train, and support your visiting students.

    Hiring a Student: Overview

    Hiring a student to clean EMR data can be a really rewarding experience.  The incentive programs for physicians provide financial rewards for better coded data.  Teams will be better able to identify candidates for chronic disease management programs.  Everyone will be better able to track progress of patients with chronic diseases and make sure they are getting the kind of follow-up they need.  From a pragmatic perspective, it will be easier to do QIP reporting and participate in D2D, adding your voice to strengthen your association’s ability to advocate for what you need.  And you may learn something too! There are many students in health programs who both want and need placements as part of their programs – they can add their energy and fresh knowledge to your team.  And finally, hiring a student may give your team an advantage in recruiting future staff, physicians or otherwise. You don’t have to start from scratch with hiring a student.  Several teams have been doing this for years, assigning students to help clean up EMR data, doing things like reconciling the roster with MOHLTC, making sure chronic diseases and risk factors like smoking are coded in the appropriate problem lists etc.  AFHTO has compiled a tool kit based on these experiences to share the learnings with other members of AFHTO.  It includes step by step guidance, starting with how to make the case for better EMR data with physicians and other decision-makers and estimating the costs and benefits of the project right down to posting and filling the position and creating the training handbook for the students.

    Checklist for Hiring A Student

    The following are the steps to consider when planning a student placement:

    Planning and Funding

    Decide that you want to clean up your historical data.

    • Why should you do this? What’s Important to YOUR Practice?
    • Budgeting and Incentive Programs
    • Sample draft physician agreement note
    • Consider the different types of students potentially available

    Recruiting A Student 

    1. Determine the specific activities you want the student to undertake and form your job description around this.
    2. Start the recruitment process: This varies according to choice of student and school.
    3. Interview and select candidates.

    Training and Hosting the Student

    1. Enroll your team to participate in/send a student to an orientation session:
    2. Prepare to host the student:
    3. Mentor/monitor student (support to be developed)

    Evaluating the Impact of the student work (More information to come) To ensure that teams are receiving value in the projects undertaken by students it is important that teams evaluate the outcomes of the  projects e.g. a clean roster, better coding of data leading to improved billings, to the time and cost of bringing in a student. For more information please contact Catherine Macdonald.

  • Getting started on a diabetes registry

    This document explains how to use a standardized query of your EMR to start building a diabetes registry.  It is intended for teams that do not yet have a reliable list of diabetes patients and don’t have the time or resources to start from scratch in reviewing all their patients to generate such a list.  Right now, it is also only for teams with PSS, Accuro and OSCAR. The following steps will help your team use the query to generate a list of diabetes patients, starting from your EMR.

    Step 1. Estimate how many patients you think this will affect.  Multiply the number of patients your team serves by 0.10 (the average rate of diabetes in Ontario) to get a rough idea of how many of your patients likely have diabetes.  If you still think this is a big enough group of patients for you to generate a registry for, carry on to step 2.

    Step 2. Import the query into your EMR.  Right now, you can only do this if have either Telus PSS or QHR Technologies Accuro EMRs.  You will likely need the help of your QIDSS, IT staff or other person who usually works with your EMR to do this.

    • For PSS, import the PSS SRX file into your EMR, click here. 
      • Click here for the case definition 
      • Click here for the screenshots of the query 
    • For Accuro, download the query “AFHTO Diabetes Frontend Search” from their publisher.
      • Click here for the case definition 
    • For OSCAR download the query and save locally to your computer.  Instructions on how to import the query into your OSCAR EMR can be found here.

    We are in the process of creating similar queries for OSCAR. Contact improve@afhto.ca. for more information.

    Step 3.  Run the query in your EMR.  Again, you might need the help of your QIDSS, IT staff or other person who usually runs queries in your EMR. Running the query will produce a list of patients with diabetes. The list will not be perfect – probably 15% of the patients identified by the query will NOT have diabetes. The query gets you STARTED in building the diabetes registry but doesn’t do the whole job for you.

    Step 4. Find the patients who might not have diabetes. Review the list of patients generated by the query to separate out those patients that are clearly already coded as having diabetes. What’s left will the list of patients who MIGHT have diabetes based on other data in the EMR besides formal coding.

    Step 5. Prepare your physicians to review the list  Subdivide the list of possible diabetes patients into separate, shorter lists for each physician. Work with your physicians to find out if they would prefer a list on paper or electronically and how they might like it sorted (i.e. by name or most recent visit or some other parameter).

    Step 6. Invite each physician to review their list of patients.  They know their patients best and can likely quickly confirm which ones do or do not have diabetes, even though that information might not be easy for others to find in the EMR.

    Step 7.  Clean up your EMR data.  Add diabetes codes to the EMR for each patient that the physician confirms as having diabetes. This so-called “data cleaning” work is a great job for a student.  AFHTO has created a toolkit to assist members in recruiting and using students for data clean-up. Click here for the toolkit.

    Step 8.  Re-run the query . After you have corrected the EMR, re-run the query to generate a list of patients with diabetes. This is your new diabetes patient registry. Going forward, you can run the query anytime you need to generate a list of diabetes patients.  You can use the list to invite patients to a diabetes health program, track progress with outcomes on these patients once you have started such a program or any other purpose. This query was produced by and for QIDSS in support of all AFHTO members.

    If you have any questions please contact improve@afhto.ca..

  • EMR queries for D2D – Childhood Immunization

    The EMR queries below were developed by QIDSS and the EMR Communities of Practice to help you extract data for submission to D2D.

    Telus PS Accuro Nightingale OSCAR P&P

     

    NOTE: All queries are tested and validated prior to release. However, changes that take place after the queries are released may affect how accurate they are. Such changes could include EMR software updates, new medications, and changes to standard clinical definitions. They may result in false positives, that is, patients being flagged who do not have the specified condition. They may also result in false negatives, that is, patients not being flagged who do have the condition. Queries are also limited by the quality of your EMR data. 

      The query criteria for childhood immunizations now reflect team-based care better:

    D2D results for this indicator may be lower than expected due to inclusion of this optional vaccine. Consider sharing your challenges and successes with your EMR Community of Practice or contact improve@afhto.ca.

    Telus PS  

    The D2D- Childhood Immunization v1 searches (.srx files) will extract data for the numerator and denominator for all children with up-to-date immunizations. Save these searches to your desktop and import into your EMR. This guide provides instructions for importing the searches along with screenshots of the searches.  You might need the help of your QIDSS, IT staff or other persons who usually run queries in your EMR. . Consider sharing your challenges and successes with the Telus PS CoP or contact  improve@afhto.ca

    Accuro 

    Please download the D2D- Childhood Immunization v1 queries from the publisher. You may need the help of your QIDSS, IT staff or any other person who usually runs queries in your EMR. Consider sharing your challenges and successes with the Accuro CoP or contact  improve@afhto.ca

    Nightingale 

    It appears that the Cumulative Bonus Report for childhood immunizations almost matches the current D2D definition. These instructions show you how to access the immunization data using the Health Maintenance (HM) report. To learn how to clean up immunization data so the HM report works effectively, have a look at this guide. Please consider sharing any feedback you might have with the Nightingale CoP or contact  improve@afhto.ca

    OSCAR 

    The D2D-Childhood Immunization v1 query generates the number and percentage of active patients 30-42 months (inclusive) who are up-to-date with immunizations. Please save the query to your computer and import into your EMR. You might need the help of your QIDSS, IT staff or any other person who usually run queries in your EMR to install the query into your EMR. Please consider sharing your challenges and successes running this query with the OSCAR CoP or contact  improve@afhto.ca

    P&P 

    If you have access to backend data for your P&P EMR, use this updated query – Childhood Immunization V2. It was originally developed by West Carleton FHT for D2D 3.0 (2015). and was updated by P&P in July 2018. Please consider sharing any front end workarounds you might have with the P&P CoP or contact  improve@afhto.ca

  • EMR data quality – Data quality actions

    Updated as of January 22, 2016

    Estimate impact of data quality

    The data are almost certainly not a definitive estimate of your team’s actual performance. However, they might be “good enough” to help you decide if your team needs to improve or not. To determine if the data are “good enough” for that, estimate how likely it is that one or more of the issues outlined in the interpretive notes are a problem with your team. Then, run the “imperfect data impact calculator” to see if the issue(s) could lead to a different decision related to the need for improvement. To do this, work with your clinical leaders and staff to establish an approximate impact of data quality – i.e. is the data quality issue causing your performance to look like TWICE or HALF or 10% (or other number) less or more than it actually is? Plug that number into the “imperfect data impact calculator”. It will show you whether the data quality issue(s) you think you have would change your initial decision regarding the need to improve. Click here to access the Imperfect Data Impact Calculator. You may find it hard to generate consensus about the impact of data quality issues on the level of performance shown in the D2D 3.0 report. In that case, consider the following options:

    • Track the next 10 (or 20 or other small number) encounters to get a better estimate of the extent of the data quality issue. Perhaps the rate among these patients will shift your team’s overall rate to be TWICE or HALF or 10% (or some other number) of the rate in the report. Plug that number into the “imperfect data impact calculator” and proceed accordingly.
    • Estimate how many of your patients eligible for cervical or colorectal cancer screening have tests or labs recorded properly in the EMR. Perhaps the rate among these patients will shift your team’s overall rate to be TWICE or HALF or 10% (or some other number) of the rate in the report. Plug that number into the “imperfect data impact calculator” and proceed accordingly.
    • Estimate how many of your patients have a smoking status coded in a consistent manner in your EMR. Perhaps the rate among these patients will shift your team’s overall rate to be TWICE or HALF or 10% (or some other number) of the rate in the report. Plug that number into the “imperfect data impact calculator” and proceed accordingly.
    • If none of the above is helpful, consider instead experimenting with possible “error” rates to see how much error (i.e. TWICE or HALF or 10% of some other number) would be needed to change the decision made on the basis of the performance of the indicator in D2D 3.0. If, in the opinion of the team, such an amount of error is reasonable, then it may be worth considering efforts to improve data quality. Alternatively, if that amount of error is considered to be unlikely, then the data are likely good enough to support the initial decision regarding the need to improve, based on the performance shown in D2D 3.0.

    If the “imperfect data impact calculator” points to the same decision (e.g. a need to improve or NOT) even after data quality issues are considered, the data are likely “good enough” to base your decision on regarding the need to improve. The next step is to consider strategies to improve, assuming the area of care measured by the indicator is a priority for your team. If your data are not “good enough”, you may then consider taking action to improve your data quality, before or at the same time as you try to improve processes of care.

    Increase quality of the data

    …if the “imperfect data impact calculator” shows that the issues in your data may point you to a different decision than suggested in D2D 3.0.

    • The goal of this indicator is to inform and motivate action to improve data quality in the EMR and also serve as a measure to monitor progress for actions to improve data quality such as those described for all the other D2D indicators.
    • The actions to improve data quality do not need to be limited to cancer screening data or smoking status data, although these might be of immediate interest. Accurately identifying deceased patients could be a focus, or accurately recording lab results in appropriate fields and in appropriate language, or coding diagnoses consistently are other areas
    • Patients served data: confirm which patients are alive and active (by whatever definition you use in your EMR/team) as rates of indicators based on incorrect denominators (i.e. all patients ever seen vs just the patients who are alive and who are active) will be incorrect.
    • Provide feedback to clinicians:
    • Consider hiring a student to help you clean up your data (see suggestions in this handbook for cleaning up your roster and smoking/alcohol status)
    • Participate in the EMR communities of practice and join your peers in developing new tools and processes for standardizing access to EMR data. Contact improve@afhto.ca to get connected.
    • Tap into external resources to support clinical process changes using PDSAs from HQO or others (also check with your QIDSS).

     

       

  • Patient Contact System – Pilot Project

    The goal of this project is to make it easier for teams to administer ongoing, consistent, patient experience surveys and otherwise engage patients in their care in meaningful ways. 10 teams are currently piloting the system. For more information check out the pilot project announcement and the FAQs or contact Marg Leyland. Keep an eye on this page for updates and success stories.

    Sept. 10, 2015: Data are starting to come in

    Five pilot teams have successfully implemented the automated patient contact system (all use Telus PS EMR) – 3 more teams (Accuro and Telus PS users) are scheduled to go live in the next 3 weeks. The system runs automatically from the EMR and is configured to contact 10 patients with recent appointments by phone a day. Typically 4-5 survey questions are asked per patient. To-date 1,842 patients have been contacted with an overall survey completion rate of 37%. If necessary, the system contacts each patient twice, with 66% of the first attempts being successful. For some reason, Thursday also seems to be a good day to get a complete survey. We are continuing to gather team and patient feedback this week to summarize the lessons learned and potential value of the system for other teams.

    May 28, 2015: Patient experience surveys by phone?

    Patients are responding nicely to surveys administered by the patient contact system being piloted by 10 of our teams. Preliminary results from 1 team indicate a 50% survey completion rate – an improvement over the 30% rate typically expected for surveys administered inside the practice.