Ultimate Blogging Championship

Author: sitesuper

  • Patient experience: involved – D2D 3.0

    Click on the following links to access: 1. Technical notes 2. Interpretive notes 3. Data quality actions – Actions and ideas to consider and discuss with clinical leads and other members of the team 4. Potential actions related to processes of care – Actions and ideas to consider and discuss with clinical leads and other members of the team  

  • Same/Next Day Appointments – D2D 3.0

    Click on the following links to access: 1. Technical notes 2. Interpretive notes 3. Data quality actions – Actions and ideas to consider and discuss with clinical leads and other members of the team 4. Potential actions related to processes of care – Actions and ideas to consider and discuss with clinical leads and other members of the team

  • EF5 Creative solutions for complex patients: different strokes for different folks

    Theme 5. Coordinating care to create better transitions

     

    Presentation Details

    • Date: 10/18/2016
    • Concurrent Session E & F
    • Time: 10:45am – 12:30pm
    • Room: Harbour C
    • Style: Presentation (information provided to audience, with opportunity for audience to ask question)
    • Focus: Balance between both (e.g. Presentation of a best-practice guideline that combines research evidence, policy issues and practical steps for implementation)
    • Target Audience: Leadership (ED, clinical lead, board chair, board member, etc.), Clinical providers, Administrative staff, Representatives of stakeholder/partner organizations

    Learning Objectives

    • Describe three clinical innovations for transitions and coordination of care for complex patients
    • Determine feasibility of adopting and/or adapting one or more of the innovations presented
    • Identify success factors in developing and spreading clinical innovations
    • Consider potential roles for their own HealthLinks initiative in supporting innovation

    Summary/Abstract

    This presentation highlights clinical innovations in transitions and coordination of care for complex patients.  It illustrates key elements of three different approaches and outlines the success factors and learnings so others with similar needs can consider implementing in their settings. The three presentations will be followed by a panel discussion to explore the role of HealthLinks structures in these innovations and themes around enablers and barriers to developing and spreading these innovations.  Briefly, the three programs to be presented are as follows:

    • Thamesview : 3 Family Health Teams located in the same LHIN who have worked closely with the community hospital and community partners will present on the Health Link High User Process.  Who the cohorts are, how the patients are identified, validated, the stratification (ie: frequent vs. long user over period of time), connection (how to connect with the patient, phone, home and office visits), monitoring (establishing Action Plan and its effectiveness) and how inactivation of the patients occurs.
    • Prescott : Health Link is an innovative initiative that provides care to the most complex patients. Personal health goals are identified and the elaboration of a coordinated care plan is shared among the circle of care. The particularity of our Health Link is the home visit by a nurse practitioner, who provide direct care to the patient, on top of coordinating care. All pertinent interventions are shared with the primary care provider.
    • Northumberland: The presentation will highlight the innovative and successful model of care that was created through a collaboration between the Northumberland Family Health Team  and Northumberland Hills Hospital  that provides interventions that reduce gaps in care for patients, with COPD/and or CHF  as they transition from the hospital to home. A team based approach to care, lead by an NP,  provides more intensive care in the patient’s homes in order to prevent hospital readmission, ER visits and to ensure positive patient experiences and positive health outcomes for the patient. 

    Presenters

    • Andrea Atkinson, Health Links Case Manager, Thamesview FHT
    • Diana Hegedus, Health Links Case Manager, Tilbury District FHT
    • Barb Lather, Business and Program Manager, Thamesview Family Health Team
    • Francois P. de Courval, NP, M.SC, Nurse Practitioner, Prescott-Russell Health Link
    • Sylvie Lemaire, Programm Manager, Prescott-Russell Health Link
    • Laurie Angione, MN-NP Adult – Lead “Home Based Transition Care Team”, Northumberland Family Health Team
    • Karen Peters, RPh, Northumberland Family Health Team
    • Joanne Jury, Access & Patient Flow Improvement Specialist, Northumberland Hills Hospital

    Authors & Contributors

    • Laura Johnson, Executive Director, Chatham Kent Family Health Team
    • Kelly Griffiths, Executive Director, Tilbury District Family Health Team
    • Denise Waddick, Executive Director, Thamesview Family Health Team
    • Nancy Snobelen, Director Partnerships & System Integration, Chatham-Kent Health Alliance
    • Audrey Larocque –A.A. – Prescott-Russell Health Link

  • Primary Care

  • 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: Coded Diagnosis – Diabetes

    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.

     

    Telus PS 

    The D2D EMR Data Quality Diabetes Coded v1 searches (.srx files) will extract data neccessary to calculate the percent of patients with diabetes whose diagnosis is recorded with a code in the appropriate place in the EMR. 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. This guide provides screenshots of the searches along with instructions on how to import the searches into your EMR. Share you challenges and successes with the Telus PS CoP or contact us for more information.

    Accuro 

    Please find the D2D EMR Data Quality DM Coded v1 numerator and denominator queries on Publisher. 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 

    The D2D EMR data quality diabetes coded denominator query based on the case definition developed CPCSSN has proven difficult to build. Please contact us if you have a query for Nightingale that you’d like to share or if you have any suggestions for this work.

    OSCAR 

    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. Download either set of  queries 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 your challenges and successes with the OSCAR CoP or contact us for more information.

    P&P 

    The D2D EMR Data Quality Diabetes Coded query is currently under development. Please contact us if you have a query for P&P that you’d like to share or if you have any suggestions for this work.

  • 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 – EMR Data Quality: Colorectal and Cervical Cancer Screening

    The EMR queries 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. 

      Remember that the cancer screening rates are for data quality purposes only. They are NOT intended as measures of performance of cancer screening. Once the EMR rates are determined you can compare them to the CCO Screening Activity Report (SAR) rates. The results will then be rolled up into the EMR Data Quality indicator. Teams are encouraged to use these results to discuss ways of standardizing data entry and cleaning up the EMR accordingly, as next steps in the QI process. You may have your own queries and criteria for local cancer screening initiatives. The queries presented below could be used to replace them if you like, but we cannot adjust the criteria here to make it more “useful” to people in the field given the purpose of the indicators to reflect data quality and as such, the need to match the SAR criteria as closely as possible. Once you have tried running these queries pleas consider sharing your challenges and successes with your EMR CoP or improve@afhto.ca.

    Telus PS 

    The D2D EMR Data Quality Cancer Screening v2 searches (.srx files) will extract data for the for up-to-date colorectal and cervical cancer screening rates. 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, along with screenshots of the searches. Share your challenges and successes with the Telus PS CoP or  contact us  if you have any questions.

    Accuro 

    The numerator and denominator queries, and instructions on how to calculate screening rates can be found in the Colorectal Screening guide and the Cervical Screening guide. You might need the help of your QIDSS, IT staff or any other person who usually run queries in your EMR. Share your challenges and successes with the Accuro CoP or contact us for more information.

    Nightingale 

    Cervical and colorectal cancer screening queries can be run in data miner as illustrated in this guide. After calculating your EMR/SAR ratio, you might consider using the Health Maintenance (HM) module to clean up your EMR. Share you experiences with the Nightingale CoP or contact us with any questions or concerns.

    OSCAR 

    Please download the D2D EMR Data Quality Cancer Screening v1 queries to your computer. Have a look at this guide for instructions on how to import the queries and screenshots – notice that a drop down menu allows you to select a specific physician depending on who you have a SAR report for. You might consider adding exclusion criteria to the queries to match the SAR criteria as closely as possible. Please contact us for more information and consider sharing your challenges and successes running the queries with the OSCAR CoP.

    P&P 

    The current HMP module in P&P does not match the SAR criteria so work continues to build a query that is not based on billing data. Please contact us if you have a cancer screening query for P&P that you’d like to share or if you have any suggestions for this work.

  • 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.

  • Optimizing Interprofessional Resources & Spreading Access to Teams: Case Study (2016)

    As government implements the vision of Patients First, the creation of sub-LHIN regions will enable a shift to a population-based approach to health care planning and delivery. It is hoped through these system-level changes patients will receive more timely access to, and better integration of, primary care, and better coordination and continuity of services. By looking at the needs of a defined population in sub regions, there is also opportunity to create more equitable access to care and to ensure appropriate care options are in place to meet community needs. Creating equitable access to team based primary care for those who would benefit Currently only 25-30% of Ontarians have access to team-based primary care. Evidence tells us with a team-based approach to primary care, patients experience more timely access to care, better care coordination and improved management of chronic diseases. The question is – How do we optimize the use of team resources to maximize access without causing undue stress on providers, unacceptable increases in wait times, and/or decreases in quality of care? In order to spread interdisciplinary team capacity more broadly in communities, careful consideration must be given to understanding population needs, making best use of existing resources, and ensuring sufficient resources to provide optimal access and quality of care. Case Study: Optimizing Interprofessional Resources & Spreading Access to Teams AFHTO, in partnership with the Osborne Group, has prepared a case study for AFHTO members which looks at how two Family Health Teams (East GTA FHT and Guelph FHT) have expanded access in their community by providing programs and services to people who were not rostered to the FHT physicians. The case study may help inform the optimal use of FHT/NPLC skills and resources and stimulate conversations amongst leadership on how we can get the best value for investment in team-based care. The case study is well aligned with AFHTO’s literature review and position paper “Optimizing value of and access to team-based primary care.” Sufficient capacity must be developed to spread access to all Ontarians Team-based primary care is already making a HUGE contribution in moving toward the vision expressed in Patients First. As we navigate through the reforms introduced we see the potential for much greater attention to the role and importance of primary care. It also reinforces the need – and creates possible mechanisms – for investment to expand team-based primary care and deliver on our membership’s vision that all Ontarians will have access to high-quality, comprehensive, interprofessional primary care. Learning from your peers: additional case studies AFHO has developed a series of case studies for our members to share the experience of colleagues on topics identified as being important to you: