Ultimate Blogging Championship

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  • Starfield Principles: Valuing Comprehensive Primary Care

    There is a compelling association between comprehensive primary care and system efficiency and effectiveness. The lifelong work of the late Barbara Starfield observed that an investment in primary care was associated with improved system quality, equity and efficiency (reduced cost). In British Columbia this efficiency was quantified by Marcus Hollander. The total cost of care was measured for the sickest patients. Patients without close alignment to primary care had a system cost of $30,000 per patient per year. Patients with close alignment to primary care had a system cost of $12,000 per patient per year.

    The Starfield Model: A Performance-Oriented Approach to Measuring Primary Care

    Comprehensive primary care is the foundation of a sustainable, responsive health care system in Ontario. The goals of comprehensive primary care are to:

    • Optimize health outcomes for patients and populations
    • Meet patient and public expectations
    • Support a sustainable health care system

    The focus of the primary care team is therefore to:

    • Improve quality
    • Increase capacity to assure access for patients
    • Reduce the total cost of care

    To be able to optimize performance of primary care teams, the foundation must be set to:

    • Support the fundamental relationship between patients and their primary care team
    • Enable primary care teams to collect and report data efficiently
    • Encourage and reinforce excellence in team performance
    • Provide the feedback needed to promote stewardship of health system resources beyond the Primary Care Team

    The key components of this model are as follows:

    • Measurement is for teams providing comprehensive primary care to a defined patient population.
    • Measurement is focused on outcomes and processes, not activities and transactions.
    • Performance is measured in terms of quality, capacity and total system cost (depicted in the model above).
    • Assessing “quality” requires simultaneous measurement of multiple indicators. In order to track overall quality over all of these dimensions, a weighted score is developed. The weighting is informed through patient engagement. This is done across a sample of patients across the primary care teams to get their input on what they value in their care, and the results will inform the choice of indicators, their weightings, and thresholds.
    • Indicators are defined by a representative body that negotiates and refines the selection and weighting of the indicators, always referring back to the relative values that the population expressed. This establishes a uniform measurement system for all of the teams.
    • Measures are adjusted to reflect the complexity in the case-mix of patients.
    • The measurement system is dynamic. Periodic review of indicators enables measurement to adapt to changing public expectations and evolving scientific evidence, thereby increasing accuracy over time.
    • Source data must be reported. This would entail reporting on each rostered/registered patient on all discrete data elements necessary to generate the desired indicator outcomes. This enables:
      • Multiple ways of analysing data and indicators.
      • Efficient verification of the accuracy of data.
      • EMR vendors do not have to analyse data.
    • Teams receive financial support to access the goods and services they require to collect and submit such data. Funds could be used for such things as EMR upgrades, electronic devices, data clerk, decision support analyst, project management. The team’s accountability is to deliver the data as a condition of funding; choices about the support needed to do so is up to the team.
    • Reporting to the participants is at the team level (not the provider level). Teams could receive provider level performance data confidentially for their internal use only. Reports will also be delivered to MOHLTC and the steering body for the pilot, with level of analysis to be determined in consultation.
    • Improvement based on internal human drive for purpose, autonomy and mastery.

    The expected benefits of implementing the Starfield Model:

    • Better value for the health care dollar.
    • Improved outcomes for patients.
    • Greater autonomy for health care providers to innovate and improve to achieve outcomes.
    • Measurement results provide greater evidence for investing in achieving the outcomes.

    The Starfield Model is named in honour of the late Barbara Starfield, researcher and champion of the value of strong primary care systems worldwide. Her name is used with permission from her family.

    Defining Comprehensive Primary Care

    In Ontario, comprehensive primary care is often described by the Provincial Co-ordinating Committee on Community and Academic Health Science Centre Relations (PCCCAR) Basket of Services. Outside of this, there is no mention of the term in Ontario’s Action Plan for Healthcare, and passing mention in the Strategic Directions for Strengthening Primary Care report. The MOHLTC website on “your healthcare options” lists “walk-in clinic” as the first option! The concept of comprehensive primary care is congruent with that of the Patient Medical Home. The US National Committee for Quality Assurance–Patient Centered Medical Home identified the key elements as follows:

    • Enhance Access/Continuity
    • Identify/Manage Patient Populations
    • Plan/Manage Care
    • Provide Self-Care Support/Community Resources
    • Track/Coordinate Care
    • Measure/Improve Performance

    Measuring Comprehensive Primary Care:

    AFHTO’s approach to primary care measurement focuses on the relationship with our patients and our ability to deliver the care patients value. Its objective is to optimize quality, access and total health system cost of care for patients, using indicators from Health Quality Ontario’s Primary Care Performance Measurement (PCPM) Framework. An article describing the model and a case study of its implementation was published in Healthcare Management Forum – The Starfield model: Measuring comprehensive primary care for system benefit. Barbara Starfield said, “Any country that is serious about primary care would eschew a sole focus on disease-oriented quality goals. Yet Canada has adopted lock, stock and barrel the ‘micro’, biomedically oriented approaches to quality, and payment for performance focused narrowly on diagnosis and management of specific diseases.” To get a true picture of the quality of comprehensive primary care, one must consider the balance of multiple indicators at the same time. The PCPM framework includes many of the key indicators that are important for identifying the key attributes and services of comprehensive primary care; however the framework includes more than 50 measures grouped under 8 domains. It will be necessary to roll up individual measures into domain summary measures in order to maximize the usefulness of the PCPM framework for practices. To facilitate comparisons between practices it will also be useful to develop an overall summary measure that includes all of the domains. To reflect the value of comprehensive primary care, it will be advisable to weight each measure according to its societal value. Appropriate weights could be established through a process that engages the public, patients, providers and decision-makers. The resulting domain and overall summary measures would then be useful measures of the value that comprehensive primary care has for society.

    Additional Resources:

  • Preventing Childhood Obesity: A Clinical Tool

    Most primary care providers in Ontario see young patients and their families who are at risk of developing obesity. We know that adolescents with obesity are more likely to be have obesity as adults and face greater risk for heart disease, stroke, some cancers and depression. We also know obesity puts patients at higher risk for more chronic diseases, like diabetes, which has a significant impact on Ontario’s health care system. The Preventing Childhood Obesity Clinical Tool was developed in response as part of Knowledge Translation in Primary Care Initiative. This tool was developed under the clinical leadership of Dr. Yoni Freedhoff (MD, CCFP, ABOM) and was designed for day-to-day use in a typical primary care setting. The Knowledge Translation in Primary Care Initiative is aimed at developing and disseminating health information and clinical tools to support primary care providers.  Its purpose is to improve engagement and enhance communication with primary care providers across Ontario and is a collaboration of the Ontario College of Family Physicians (OCFP) and the Nurse Practitioners’ Association of Ontario (NPAO) and the Centre for Effective Practice (CEP). Relevant Links:

  • Couchiching FHT to be named 2016 LEADing Practice

    Congratulations to the Couchiching Family Health Team for being named a 2016 LEADing Practice Initiative. The FHT is being recognized for exemplary use of digital tools to strengthen clinical practice and provider experience. Couchiching FHT’s project integrated a tablet-based system into their EMR to screen patients at risk for chronic obstructive pulmonary disease (COPD). This also allowed patients to edit demographic information stored in their patient chart in the EMR in real time, without any increase in staff time or resources. Presented at the AFHTO 2015 Conference, benefits of the system included approximately 40% (or 3000) of the total patient population completing smoking screening in a little over 4 months compared to the previous, paper-based COPD screening process in which only 200 patients were screened at baseline. There was also a 33% increase in referral to the smoking cessation program. Couchiching is now expanding the tablet program to further identify and support individuals with other chronic diseases e.g. diabetes, and using such screenings for depression. The LEADing Practice Initiative, a partnership between Canada Health Infoway and Accreditation Canada is part of a larger Clinician Education Campaign, identifying LEADing practices across Canada that demonstrate the clinical benefits of digital health. Their award will be presented at the 2016 Peer Leader Symposium: Building Peer Leader Bridges to Advance Clinical Practice event to be held on March 3-4, 2016 at the Intercontinental Toronto Centre in Toronto. Relevant Links:

  • Primary Care’s united response to Minister’s Patients First proposal

    In a letter to Minister Eric Hoskins six associations of the Ontario Primary Care Council (OPCC) have provided initial feedback on the Province’s plans to strengthen our health care system. The Council recommends that the Ministry of Health and Long-Term set out clear principles for planning aligned with OPCC’s Framework for Primary Care in Ontario, develop a plan to embed care coordinators in primary care, address the role of primary care in mental health and palliative care, and ensure a consistent primary health care population needs-based planning approach across all fourteen LHINs. Click here to read the letter submitted on January 22, 2016, in response to the Minister’s Patients First proposal, released December 17, 2015. AFHTO will continue working with members and our provincial partners to develop complete responses to the Patients First proposal.

  • Response to Ministry’s Patients First proposal: current status

    This email summarizes current status and next steps in developing a response on behalf of AFHTO members to the Ministry’s Patients First  proposal.

    Provincial-level action:

    Regional-level action:

    AFHTO members are meeting with their LHINs and working together to strengthen the primary care voice within the LHIN. AFHTO has offered support. To date AFHTO has organized 12 meetings in 10 LHINs between FHT/NPLC leaders and their LHIN CEOs. The Ministry has also invited feedback from health care providers, patients and caregivers by February 29.

    Membership input to date:

    Click here to see full report on all topics arising from the 14 web meetings.

    The top three topics were:

    • Accountability & Contractual Relationships
    • Support for Leadership Roles / Smooth Transitions
    • Primary Care HHR Planning

    Physician Leadership Council meeting:

    Click here for the full report on Physician Leadership Council meeting.

    PLC members discussed the first two topics. Dr. Sarah Newbery, OCFP President and FHT physician, joined the meeting to receive input on the OCFP’s initial work on clinical leadership. On the issue of accountability to MOHLTC vs LHIN, views were mixed, with valuable input provided as to what is most important going forward.

    Overall, the Chair summarized the top three messages from this meeting as:

    • The FHT structure fosters development of clinical leadership. Education and support is needed to further develop clinical leadership and extend it more broadly beyond teams.
    • Make change slowly – pay attention to the “critical success factors” to ensure change achieves desired improvement.  Keep accountability clear.
    • Quality improvement is fundamental to all we do in primary care, and physician leadership is essential to doing this.

  • Childhood Immunization – Potential actions related to processes of care

    Assuming you have established that the data are good enough to direct action AND that improving performance in this area is a priority for your team, you may wish to discuss the following options with your clinical leaders, Quality Improvement committees, team staff and/or patients:

    • Interventions to improve immunization rates through conversation with Public Health units and others
    • Contact your peers to determine their performance and work with them to either spread any processes they find have helped them or collaboratively test some new changes that might work for you AND your peers.

  • Childhood Immunization – Data quality actions

    Estimate the impact of data quality

    • Access the Imperfect Data Impact Calculator to find out whether the data quality issue(s) you think you have would change your initial decision regarding the need to improve.
    • Please see below for more information about this tool.

    Increase the 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. You might consider the following:

    • Work with engaged clinicians to increase the consistency of data entry related to immunization. Refer to the Standardized EMR queries for guidance on ways to increase consistency in data entry such that extraction of the data can be more efficient and accurate over time and between teams.
    • Track your immunization rates on an ongoing basis vs just at year-end. Work with QIDS Specialists in the application of consistent queries across teams and EMRs to increase access to these data in an efficient way.
    • Ask patients about their immunizations, partly to update the EMR and partly to engage them in an important discussion about preventive strategies. Tilbury District FHT uses a simple paper form for patients to share information regarding flu shots received elsewhere. A similar idea could be applied to childhood immunizations. Better yet, contact them directly to find out exactly how this works!
    • Support AFHTO’s efforts to work for inclusion of a standard process for recording and reporting immunization data aligned with PHAC guidelines as part of the technical specification for EMRs. This is already in progress through the EMR Data Management committee and individual EMR Communities of Practice.
    • Develop data sharing processes with public health units and other partners engaged in immunization to ensure accurate records regarding immunization status, not only for reporting purposes but also for better risk management for patients.
    • Other ideas: please share!

    Additional  information for estimating the impact of data quality for this measure:

    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 above 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 get their immunizations elsewhere. 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 you are having trouble estimating how many patients are getting immunizations/flu vaccines elsewhere you might want to use 43% as a ball park figure. This was the rate observed by Tilbury District FHT who use a simple paper form for patients to share information regarding flu shots received elsewhere.]
    • Estimate how many of your patient’s decline immunization when offered. 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.

  • Childhood immunization – all children – Technical notes

    Please refer to the D2D 3.0 Data Dictionary

  • Childhood Immunization – Interpretive notes

    • The D2D 3.0 definition fully aligns with Public Health criteria. This means Rotavirus is now included in the definition and EMR queries. It is not part of the preventive care bonus; therefore, performance may appear lower than in your preventive care bonus reports which exclude Rotavirus.
    • The D2D definition does not reflect patient choice. Patients who decline immunization and therefore are not immunized may be the reason why your rates are lower than you expect. See “data quality actions” for ideas to examine the extent to which this is affecting your rates.
    • Data for patients immunized outside of the primary care team (e.g. at a health unit) might not be recorded consistently in all EMRs and teams. The performance seen in D2D 3.0 might therefore under-estimate actual immunization rates. See “data quality actions” for ideas to examine the extent to which this is affecting your rates.
    • Rates for teams with very few children in their panel may be more variable than rates based on larger eligible patient populations. For example, if 2 less children are immunized this year out of a population of 10 children, your immunization rate will drop by 20%. However, if 2 less children out of 100 are not immunized, your rate will only drop by 2%. So consider how many children are eligible for immunization when interpreting differences for your team year to year or relative to another team.
    • D2D 3.0 includes data for all children as opposed to only rostered children. This might generate different rates than those the team might be used to seeing in reports based on rostered children only (e.g. the MOHLTC Preventive Care Target Population/Service Report (TPSR)). The MOHLTC provides eligible physicians in Patient Enrolment Models (PEMs) with a Projected Preventive Care Target Population/Service Report semi-annually, in April and September, to assist them in determining their Target Population and the delivery of preventive care services.
    • The timing of D2D reporting may not coincide with the reporting time period for the MOHLTC Preventive Care Target Population/Service Report. There may be differences in rates related to these differences in time periods.
    • Although a consistent definition was developed to create queries that were shared among members for purposes of extracting these data for D2D 3.0, it is possible that the extent to which these data were consistently recorded and therefore extracted in your team might vary. To explore this issue, look at the processes used in your team to record and extract immunization data and work to align it as much as possible with the standard process being developed by QIDS Specialists.

  • Data to Decisions eBulletin #27: D2D 3.0 report goes live Feb.1

    Sign up for the Feb. 1st launch of the D2D 3.0 report for a look at how your team stacks up with the nearly 120 teams that contributed data. Also, get a first look at the new indicators for D2D 4.0. Calling all EDs! Please complete the QI Capacity survey by Feb. 7, 2016 – to help answer questions about the causes and effects of being a high performing team. REGISTER NOW: for a webinar jointly presented by AFHTO and the Ministry to review the Annual Operating Plan (AOP) timelines and expectations, with a specific focus on the Program Planning & Evaluation Framework, Indicator Catalogue and Schedule A reporting requirements for FHTs. Click here to register and pick the session you wish to attend: February 10th 12:00 – 1:30 pm OR February 17th 2:30 – 4:00 pm

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    2016-01-21 D2D timeline