Quality in Primary Care Open Access

Keywords

Quality Improvement, Chronic Disease, Patient Care, Patient Reported Outcomes, Electronic Medical Records, Sustainable Development

Introduction

Managing chronic diseases in healthcare is challenging as patients require consistent, joined-up support and personalised treatment over the long term [1]. The practice of quality improvement (QI) - the use of formal or informal tools to assess and improve the quality of the care patients receive, is a key part of optimising patient management in this setting [2–4].

Whilst there is much enthusiasm for QI as a key route to improving patient outcomes, in practice it can be much more difficult to implement and make routine. Central to this is the variability in approaches to and needs of QI [4,5]. and the perceived lack of time and competing resources required to do it well [3,6]. Despite a proliferation in the QI literature particularly on broader issues of success and sustainability [7-9], there remains a gap for practical, implement solutions for time-poor clinicians.

Optimum Patient Care (OPC) is a non-profit social enterprise founded in 2005 (Figure 1), to work alongside healthcare providers to deliver sustainable chronic disease QI programmes. OPC delivers an evidence-based, guideline-driven, expert- led and general practice informed implementation strategy to deliver effective QI. Here we discuss the successes and lessons learnt in addressing some of the key barriers to sustainable QI.

Figure 1: Optimum Patient Care: Locations and areas of focus for supporting quality improvement.

The QI Cycle: Common Barriers to Sustainable QI and Solutions

We approach QI as a cycle of activities (Figure 2)which: first seeks to understand the context and needs of the healthcare setting (step 1), reflect on these needs in parallel with national or international standards for care (step 2), work with practitioners to set achievable and measurable targets (step 3), implement change (step 4), re-evaluate the care provided and embed QI in routine practice (step 5).

Figure 2: The Cycle of Quality Improvement in Primary Care: Barriers and OPC solutions.

Step 1: Current Practice: Demonstrating the need for improvement using practice and patient data can be a persuasive tool to engage staff in QI [6]. Physicians often cite lack of buy- in or resources as reasons for poor engagement with QI [6,7] a shortage of the skills required to harness the wealth of clinical data they produce [8,9] and/or little understanding of how to involve patients in these processes [10]. To support practices in the assessment of the current state of care provision and potential areas for improvement, we have developed simple, automated tools to collect and assess data from electronic medical records (EMR) and patient questionnaires (patient reported outcomes/ information – PRO/I).

As data privacy is a legitimate concern, we take a strict approach to de-identifying data – we do not collect any practice/ patient identifiers; we use irreversible hashing algorithms to pseudonymise patient identifiers (IDs), and in the absence of inexpensive commercial options, we developed a robust custom redaction tool (https://optimumpatientcare.org/redaction/) to redact free text data to further enhance anonymity.

Step 2: Reflect on Current Standards: In an environment of continuously updated guidelines and “pay for performance” funding, clinicians struggle to keep abreast of recommendations and best-practice [11]. Standards that are linked to financial incentives can be limited in scope, focused on a select group of patients where exception-reporting (exclusion of patients from formal audit or QI) [12]. may mean that key groups of excluded patients do not benefit from improved care practices. Using the latest guidance informed by our steering committees of clinical experts [13,14]. We summarise guidelines/standards in clear and accessible formats for practitioners, which are reviewed against data collected for the practice. Following feedback from the clinicians and experts, our programmes provide recommendati- ons for both broad groups and individual patients, reflecting current, local or national guidance.

Step 3: Establish Targets: Like many others, we have found that setting targets for QI is a difficult, protracted process requiring colleagues to overcome a lack of consensus in choosing which problems to address [6]. Often compounded by previous negative experiences with overambitious targets [2]. Our evolutionary approach helps to set reasonable targets by using practice and patient information to describe current practice, comparing this to local, national or international standards coupled with documented histories of achievable targets successfully implemented by providers in similar situations. We provide digital templates for standardised data entry, practice and patient level reports which are simple and clear with visualisations; summarising current care and recommending measurable targets for improvement. While we develop our own feedback system, we use pre-existing work when appropriate. An example target is the identification of high-risk asthma patients which may be ‘hidden’ to clinicians: We have implemented a peer- reviewed automated risk-prediction algorithm [15] that scores each patient on the likelihood of future asthma attacks (Figure 3) and have used the validated Target COPD algorithm to identify patients at risk of COPD.

Figure 3: Application of the Respiratory Effectiveness Group (REG) algorithm to predict the risk of future exacerbations in patients with asthma in quality improvement programmes: Excerpt from an anonymised OPC UK asthma patient report showing individualised output of the asthma risk predictor (yellow box).

Step 4: Implement Change: We have learnt that clinicians respond well to patient stories. Patients also feel listened to when they are invited to see their GP after completing an OPC questionnaire, or when provided with an individualised report. Evidence shows that routinisation of QI activities like these is key to sustainability [16]– thus our reports and templates are designed to be embedded in routine care and to support everyday clinical decision making. This process has grown out of experience and requires technical infrastructure and expertise, which is not always readily available to practices. A key lesson was to move from simply implementing systems that support data-driven QI, to also maintaining and developing them for the healthcare providers we support.

Step 5: Re-evaluate: An important motivator for continuing QI is the “I” - “Improvement” aspect – the ability to demonstrate improvement and the value of what has been achieved [10,11]. Assessing the impact or success of a QI programme is no small undertaking. Figure 2 step 5 highlights several reoccurring themes, summarised as a requirement for both resources and a willingness to re-engage with the cycle. Our evaluations are time-bound to maintain momentum and are based on periodic re-extraction of EMR and PRO/I data and auto-generation of reports to help healthcare providers track their improvements. A recent evaluation of our chronic obstructive pulmonary disease (COPD) QI programme in UK demonstrated that practices which implemented the programme saw an overall 20% reduction in the proportion of high risk patients having a COPD exacerbation in the 12m following the start of the QI programme, compared to 10% reduction across all practices not actively doing QI [7,17-21].

Conclusion

It is increasingly recognised that healthcare practitioners, patients, the health service and the economy can benefit from improvements in patient care for chronic diseases. We have shown that large scale, collaborative QI programmes can have clear measurable benefits with little impact on workload. Following a decade of refinement of our chronic disease QI model, we have learnt that working alongside primary care clinicians to integrate automated, non-resource-intensive programmes that involve both clinic staff and patients can be a highly effective means to promote a long term culture of QI.

Submission Declaration

This contribution is original. The work has not been published previously and is not currently under evaluation by another journal.

Ethical Approvals

Quality Improvement programs do not fall under ethical approvals.

Acknowledgements

We would like to thank all authors, contributors, and reviewers for the development of this article. Additional thanks to the publishers for their support in the publication process. A large amount of respect and appreciation goes to the practices who undergo continuous quality improvement, through participation in this program. Their wish to contribute to the advancement of this subject of interest, helps to provide greater standards of care to their patients.

Source of Funding

The OPC QI program is fully funded by OPC Global Lt

Conflicts of interest

Alexander Evans, Seyi Soremekun, Brooklyn Stanley, Francis Appiagye, Amy Couper, Oliver Taylor, Rachel Pullen, Sophie Jones, Victoria Carter and Chris Price are employees of Optimum Patient Care and the Observational and Pragmatic Research Institute, which has conducted research in respiratory disease funded by the following organizations in the past 5 years: Anaxys, Astra Zeneca, Boehringer Ingelheim, British Lung Foundation, Chiesi, Circassia (formerly Aerocrine), GlaxoSmithKline, Harvey Walsh, Mapi, Morningside Healthcare, Mundi pharma, Mylan (formerly Meda), Napp, Novartis, Orion, Plymouth University, Regeneron, Respiratory Effectiveness Group, Roche, Sanofi, Takeda, Teva, University of East Anglia, Zentiva (a Sanofi company).

Rupert Jones reports grants, personal fees, and non-financial support from AstraZeneca and OPRI, personal fees and non- financial support from Boehringer Ingelheim, grants, personal fees, and non-financial support from GSK, grants and non- financial support from Novartis, non-financial support from Nutricia, and personal fees from Pfizer outside the submitted work.

Thao Le declares educational grants from AstraZeneca, Bayer, Boehringer Ingelheim, Care Pharmaceuticals, GlaxoSmithKline, Mylan, Novartis and Teva.

Kerry Hancock declares no conflict of interest.

Sinthia Bosnic-Anticevich has received honorarium for participation in expert advisory boards and given lectures for Teva Pharmaceuticals, AstraZeneca, GSK, Mundi pharma, Sanofi, Mylan and received unrestricted research grants from Mylan, AstraZeneca, Teva and Mundi pharma International.

Dermot Ryan has (in the last 3 years) lectured on behalf of, received sponsorship from, or acted as a paid advisor to Mylan, AZ, Chiesi, Novartis, GSK, Boehringer Ingelheim and Regeneron.

David Price has board membership with Amgen, AstraZeneca, Boehringer Ingelheim, Chiesi, Circassia, Mylan, Mundipharma, Novartis, Regeneron Pharmaceuticals, Sanofi Genzyme, Teva Pharmaceuticals, Thermofisher; consultancy agreements with Amgen, AstraZeneca, BoehringerIngelheim, Chiesi, GlaxoSmithKline, Mylan, Mundipharma, Novartis, Pfizer, Teva Pharmaceuticals, Theravance; grants and unrestricted funding for investigator-initiated studies (conducted through Observational and Pragmatic Research Institute Pte Ltd) from AstraZeneca, BoehringerIngelheim, Chiesi, Circassia, Mylan, Mundipharma, Novartis, Pfizer, Regeneron Pharmaceuticals, Respiratory Effectiveness Group, Sanofi Genzyme, Teva Pharmaceuticals, Theravance, UK National Health Service; payment for lectures/speaking engagements from AstraZeneca, Boehringer Ingelheim, Chiesi, Cipla, GlaxoSmithKline, Kyorin, Mylan, Mundipharma, Novartis, Regeneron Pharmaceuticals, Sanofi Genzyme, Teva Pharmaceuticals; payment for the development of educational materials from Mundipharma, Novartis; payment for travel/accommodation/meeting expenses from AstraZeneca, BoehringerIngelheim, Mundipharma, Mylan, Novartis, Thermofisher; funding for patient enrolment or completion of research from Novartis; stock/stock options from AKL Research and Development Ltd which produces phytopharmaceuticals; owns 74% of the social enterprise Optimum Patient Care Ltd (Australia and UK) and 74% of Observational and Pragmatic Research Institute Pte Ltd (Singapore); 5% shareholding in Timestamp which develops adherence monitoring technology; is peer reviewer for grant committees of the Efficacy and Mechanism Evaluation programme and Health Technology Assessment; and wasan expert witness for Glaxo Smith Kline.

References

1. Reynolds R, Dennis S, Hasan I, Slewa J, Chen W, et al. A systematic review of chronic disease management interventions in primary care. BMC Fam Pract 2018; 19:11.
2. McCallum M, McNab D, Luty S, Bowie P, McWalter G, et al. Quality Improvement in Primary Care: What to do and how to do it. NHS Education for Scotland 2018; 47.
3. Dawda P, Jenkins R, Varnam R. Quality improvement in general practice. 32.
4. NHS England. An Introduction to Quality Improvement in General Practice. Internet 2019; 9.
5. Ling T. Involving primary care clinicians in quality improvement: an independent evaluation of the Health Foundation’s engaging with quality in primary care programme : final report. London G.-B : Health Foundation 2012.
6. Dixon-Woods M, McNicol S, Martin G. Ten challenges in improving quality in healthcare: Lessons from the Health Foundation’s programme evaluations and relevant literature. BMJ Qual Saf 2012; 21:876–884.
7. Kaplan HC, Brady PW, Dritz MC, Hooper DK, Linam WM, et al. The Influence of Context on Quality Improvement Success in Health Care: A Systematic Review of the Literature. Milbank Q 2010; 88:500–59.
8. Robert G, Sarre S, Maben J, Griffiths P, Chable R. Exploring the sustainability of quality improvement interventions in healthcare organisations: a multiple methods study of the 10-year impact of the ‘Productive Ward: Releasing Time to Care’ programme in English acute hospitals. BMJ Qual Saf 2020; 29 : 31–40.
9. McCalman J, Bailie R, Bainbridge R, McPhail-Bell K, Percival N, et al. Continuous Quality Improvement and Comprehensive Primary Health Care: A Systems Framework to Improve Service Quality and Health Outcomes. Front Public Health [Internet] 2018; 6.
10. Parchman ML, Anderson ML, Coleman K, Michaels LA, Schuttner L, et al. Assessing quality improvement capacity in primary care practices. BMC Fam Pract 2019; 20:103.
11. Greiver M. Do electronic medical records improve quality of care? Can Fam Physician 2015; 61: 847–849.
12. Manca DP. Do electronic medical records improve quality of care? Can Fam Physician 2015; 61:846–847.
13. Jabbal J. Embedding a culture of quality improvement ;3.
14. Chin L, Satell G. How Physicians Can Keep Up with the Knowledge Explosion in Medicine. Harvard Business Review2016; Availablefrom:https://hbr.org/2016/12/how-physicians-can-keep-up-with-the-knowledgexplosion-in-medicine.
15. NHS Digital. Quality and Outcomes Framework. Achievement, prevalence and exceptions data 2018-19; https://digital.nhs.uk/data-and-information/publications/statistical/quality-and-outcomes-framework-achievement-prevalence-and-exceptions-data/2018-19-pas/frequently-asked-questions.
16. Steering Committee – OPC [Internet]. 2020; Available from:  https://optimumpatientcare.org/clinical-experts/
17. SteeringCommittee – OPCAU[Internet].2020; Available from: https://optimumpatientcare.org.au/who-we-are/our-team/.
18. Blakey JD, Price DB, Pizzichini E, Popov TA, Dimitrov BD, et al. Identifying Risk of Future Asthma Attacks Using UK Medical Record Data: A Respiratory Effectiveness Group Initiative. J Allergy Clin Immunol Pract 2017; 5:1015-1024.
19. Haroon S, Adab P, Riley RD, Fitzmaurice D, Jordan RE. Predicting risk of undiagnosed COPD: development and validation of the Target COPD score. Eur Respir J 2017;49.
20. Greenhalgh T. Role of routines in collaborative work in healthcare organisations. BMJ 2008; 337:a2448–a2448.
21. Balasubramanian BA, Marino M, Cohen DJ, Ward RL, Preston A, et al. Use of Quality Improvement Strategies Among Small to Medium-Size US Primary Care Practices. Ann Fam Med 2018;16:35–43.