Tag: Primary Care

Identifying Potential Biases in Diagnostic Codes in Primary Care Electronic Health Records: What We Need to Know

Electronic healthcare records (EHRs) are increasingly being used to collect and store data on patient care. This data can be used for a variety of purposes, such as improving clinical care, conducting research, and monitoring population health. However, it is important to be aware of potential biases in EHR data, as these can lead to inaccurate or misleading results..

The reliability of diagnostic codes in primary care EHRs is a subject of ongoing debate and a topic we investigated in paper published in BMJ Open.

These codes not only guide clinical decisions but also shape healthcare policies, research, and even financial incentives in the healthcare system. A recent retrospective cohort study explored whether the frequency of these codes for long-term conditions (LTCs) is influenced by various factors such as financial incentives, general practices, patient sociodemographic data, and the calendar year of diagnosis. The study comes at a crucial time, shedding light on significant biases that need to be addressed.

Key Findings

The study, which involved data from 3,113,724 patients diagnosed with 7,723,365 incident LTCs from 2015 to 2022, revealed some significant findings:

Influence of Financial IncentivesConditions included in the Quality and Outcomes Framework (QOF), a financial incentive program, had higher rates of annual coding than those not included (1.03 vs 0.32 per year, p<0.0001).

Variability Across GPs: There was a significant variation in the frequency of coding across different General Practices, which was not explained solely by patient sociodemographic factors.

Impact of Sociodemographic factors: Higher coding rates were observed in people living in areas of greater deprivation, irrespective of whether the conditions were part of QOF or not.

Covid-19The study noted a decrease in code frequency for conditions that had follow-up time in the year 2020, likely due to the COVID-19 pandemic affecting healthcare services.

Implications for Healthcare Providers and Researchers

The findings of the study raise some pertinent questions:

Addressing Financial Incentives: If the QOF influences coding rates, how can we ensure a level playing field for conditions not included in such programs? This could impact resource allocation and healthcare planning.

Standardizing Practices: The variability in coding across GPs implies that there might be inconsistencies in how conditions are diagnosed and recorded. These inconsistencies need to be addressed to improve the quality of healthcare.

Considering Sociodemographic factors: The influence of patient sociodemographic factors suggests a need for tailored interventions, especially in areas with higher deprivation levels.

Navigating Pandemic-related Challenges: The reduction in coding during the COVID-19 pandemic indicates that external factors can significantly affect healthcare data. This demands adaptive strategies to ensure the ongoing reliability of EHRs.

Conclusions and Future Steps

As we move towards a more data-driven healthcare system, understanding the biases in primary care EHRs becomes crucial. The study suggests that natural language processing or other analytical methods using temporally ordered code sequences should account for these biases to provide a more accurate and comprehensive picture. By doing so, healthcare providers and policymakers can better tailor their strategies, ensuring more effective and equitable healthcare delivery.

The Number Needed to Treat: Why is it Important in Clinical Medicine and Public Health?

You will often see the NNT mentioned in clinical guidelines; and when different health interventions are being prioritised or assessed for their clinical effectiveness and cost effectiveness. For example, the NNT was used to inform decisions to recommend statins for people with an elevated risk of cardiovascular disease.

The NNT is a measure used to quantify the effectiveness of an intervention or treatment. It is the average number of patients who need to be treated with a particular therapy for one additional patient to benefit.

How is NNT calculated?

In mathematical terms, the NNT = 1/[Absolute Risk Reduction]

Where Absolute Risk Reduction (ARR) = Control Event Rate (CER) – Experimental Event Rate (EER)

Control Event Rate (CER): The rate of an outcome in a control group.

Experimental Event Rate (EER): The rate of an outcome in an experimental group treated with the intervention.

For example, consider a drug that reduces the risk of heart attack from 4% to 2%. The ARR is 2% or 0.02 and the NNT is 50 (1/0.02). Hence, on average, 50 people will need to be treated to prevent one heart attack.

Importance in Clinical Medicine

The NNT is important in clinical medicine because it helps in the evaluation of the efficacy of treatments by offering a direct, patient-centred measure. It is also helpful in clinical decision making as it allows doctors and patients to make makes evidence-based decisions on treatment options. For example, when presented with data on the NNT, patients can consider how useful a medical intervention is for them.

The NNT also helps in the assessment of the balance between potential benefits and harms of treatment; and provides a uniform metric for comparing the effectiveness of different treatments.

Role of NNT in Public Health

The NNT is also important in public health because it provides a metric that can be used at a population level, offering insights into public health strategies; for example, it can help policy makers determine the most efficient use of healthcare resources. When combined with other metrics, the NNT can be a tool in assessing the cost-effectiveness of public health interventions such as preventive measures, screening and vaccination.

For example, the NNT was used by the UK JCVI to decide which population groups should be prioritised for booster Covid-19 vaccinations by considering how many people in different age groups would need to be vaccinated to prevent one hospital admission.

Limitations of NNT

The NNT does have some limitations. For example, it does not account for side effects or adverse reactions to medical interventions. It is also specific to the particular patient populations and settings from which the data to calculate the NNT was derived. For example, many adverse health outcomes are more common in older people. Hence, the NNT is not uniform over the population and will be lower in groups at higher risk such as the elderly.

Conclusions

Understanding NNT is crucial for both individual clinical decisions and broader public health strategies aimed at population health improvement. It provides an intuitive way to understand the practical impacts of treatment and public health interventions; and is a measure that is useful to many groups including policy makers, clinicians, public health specialists and patients.

The Impact of Virtual Consultations in Primary Care

Virtual consultations have increased in healthcare in recent years, especially since the onset of the COVID-19 pandemic. While telehealth offers many benefits for patients, such as convenience and increased accessibility, questions surrounding its impact on the quality of primary care persist. Our recent systematic review “The Impact of Virtual Consultations on the Quality of Primary Care” offers valuable insights into this timely and topical issue in healthcare delivery.

The primary goal of the study was to evaluate how virtual consultations are influencing the quality of primary care. The study was comprehensive, covering various diseases and utilizing six databases for identifying studies. It employed a rigorous screening process to ensure that only pertinent data was included.

Key Findings

The review included 30 studies comprising 5,469,333 participants. The results were quite revealing:

1. Effectiveness: Virtual consultations were as effective, or even more so, than traditional face-to-face consultations for managing certain conditions such as mental illness, smoking, and excessive alcohol consumption.

2. Patient-Centeredness: Four studies showed positive impacts on patient-centeredness, although patients felt a decrease in perceived autonomy support when engaging with healthcare providers virtually.

3. Efficiency: Virtual consultations might reduce waiting times, decrease patient costs, and lead to fewer follow-ups in secondary and tertiary healthcare settings.

4. Patient Safety: Unfortunately, data on the impact of virtual consultations on clinical safety was found to be extremely limited.

5. Equity: The evidence is mixed regarding the equitable use of virtual consultations. They seem to be favoured more by younger, female patients, and disparities were observed among other demographic groups depending on contextual factors.

Areas for Further Research

The study identified several gaps in the existing body of evidence. Specifically, there is a need for more robust data regarding patient safety, equity, and patient-centeredness. The researchers stress the importance of utilizing real-world data and clinical trials to ensure that virtual consultations are both effective and inclusive.

Conclusions

While the systematic review brings optimism about the effectiveness and efficiency of virtual consultations, it also flags important areas where more research is needed. A tailored approach, based on more comprehensive data, is crucial for informing future policies in virtual primary care. By focusing on these areas, healthcare providers and policymakers can aim to offer a more balanced, equitable, and safe healthcare delivery system for patients.

Direct access to cancer diagnostics: the promise and perils of bypassing GPs

The Secretary of State for Health and Social Care, Steve Barclay, has confirmed the UK government is considering plans to allow patients in England to bypass their GP and directly access some diagnostic tests for suspected cancer. The clinical and cost-effectiveness of these new diagnostic pathways must be compared with alternative solutions such as investing more in core NHS general practice services. My article in the British Medical Journal discusses some of the key issues and challenges in implementing this radical new policy.

Electronic health records: The importance of implementation and training

A new article in the British Medical Journal from Carol Chan, Ana Neves and myself looks at the importance of implementation and training in the use of electronic health records (EHRs) in healthcare. The introduction of EHRs has been one of the most significant changes in how healthcare is delivered in recent decades. But while EHRs have brought many benefits to the NHS, for patients and clinicians, they have also created substantial challenges that must be addressed.

The effects of community interventions on unplanned healthcare use in patients with multimorbidity

Multimorbidity, the coexistence of multiple chronic conditions within an individual, is a growing global health challenge affecting a significant portion of the population. Patients with multimorbidity often face complex healthcare needs, leading to increased unplanned healthcare utilization. In an effort to address this issue, community-based interventions have emerged as potential solutions for providing continued care outside of traditional hospital settings. Our systematic review published in the Journal of the Royal Society of Medicine aims to summarize the impact of these interventions on unplanned healthcare use in patients with multimorbidity.

The Burden of Multimorbidity

With the prevalence of multimorbidity increasing, affecting approximately one-third of the global population, it is crucial to find effective strategies to manage this complex condition. The challenges posed by multimorbidity often result in frequent emergency department visits and hospital admissions, placing a significant strain on healthcare resources.

Community-Based Interventions

Community-based interventions offer a promising approach to address the needs of multimorbid patients. These interventions focus on delivering care in community settings, with an emphasis on education, self-monitoring of symptoms, and regular follow-ups. Additionally, some interventions aim to improve care coordination, advance care planning, and provide palliative care for patients with severe conditions. By implementing these interventions, healthcare providers seek to enhance patient self-management, reduce the burden on emergency departments, and improve overall health outcomes.

Findings from the Systematic Review

Thirteen studies, involving a total of 6148 participants, were included in this systematic review. Notably, all the studies were conducted in high-income settings and primarily focused on elderly people. The primary outcome assessed across all studies was emergency department attendance. The risk of bias was generally low across the included studies.

The results revealed that all 13 studies reported a decrease in emergency department visits following the implementation of community-based interventions. The risk reduction ranged from 0 (95% confidence interval [CI]: –0.37 to 0.37) to 0.735 (95% CI: 0.688–0.785). This suggests that these interventions have the potential to effectively reduce unplanned healthcare usage among patients with multimorbidity.

Challenges and Future Directions

Identifying specific successful components of community interventions proved challenging due to the overlaps between different interventions. However, the overall findings strongly support the integration of community-based approaches into existing healthcare structures. Policymakers should recognize the importance of these interventions and work towards their implementation to alleviate the burden on emergency departments and improve patient outcomes.

Future research must explore the impact of community interventions on a broader range of participants. This will allow for a better understanding of the effectiveness of these interventions in diverse populations and settings. By expanding the scope of research, we can gain deeper insights into the potential benefits of community-based interventions for patients with multimorbidity.

Conclusion

Community-based interventions have shown promise in reducing emergency department visits among patients with multimorbidity. These interventions empower patients to manage their conditions, promote education, and improve care coordination. Policymakers and healthcare providers should recognize the value of these interventions and work towards integrating them into existing healthcare structures. By doing so, we can enhance patient care, reduce healthcare costs, and alleviate the burden on emergency departments. As we move forward, further research is needed to explore the broader impact of community interventions and their potential to improve outcomes for patients with multimorbidity in various contexts.

The Future of the Quality and Outcomes Framework (QOF) in England’s NHS

The Quality and Outcomes Framework (QOF) was introduced in 2004 as part of a new NHS GP contract with the aim of financially rewarding general practices for delivering evidence-based standards of care. While initially unique internationally, the QOF in the UK is now facing uncertainty, with calls to cut it back or abolish it due to various challenges faced by the NHS. In an article published in the journal BJJP Open, Mariam Molokhia and I discuss the role of the QOF in England’s NHS and argue for its importance in improving health outcomes and addressing public health challenges.

The Importance of Comprehensive Health Services

Primary care plays a vital role in providing comprehensive health services, covering both acute and long-term conditions. Beyond immediate patient needs, the focus should be on prevention, early diagnosis, and management of chronic diseases that contribute significantly to ill health, reduced quality of life, and increased NHS workload. Amid the COVID-19 pandemic, urgent care rightfully took precedence, but it is now crucial to restore high-quality care for long-term conditions.

The Role of QOF in Addressing Public Health Challenges

Public health challenges have underscored the importance of the QOF, especially in areas focused on secondary prevention and long-term condition management. Meeting QOF targets for conditions like type 2 diabetes leads to lower mortality rates, reduced emergency hospital admissions, and improved health outcomes. By using the QOF effectively, the NHS can alleviate pressures on other healthcare sectors and improve patient well-being.

Data Measurement and Research 

The QOF also facilitates data collection and measurement of healthcare quality, essential for planning health services, addressing health inequalities, and ensuring efficient use of public investments. The structured data entry required for QOF enables its use for clinical research, as shown during the COVID-19 pandemic. Abolishing or significantly cutting back the QOF would have far-reaching negative consequences, undermining these benefits.

Supporting Primary Care Teams and Addressing Challenges

Rather than discarding the QOF, it is crucial to support primary care teams in delivering structured care while addressing urgent patient needs. Adequate funding, including a review of funding allocation mechanisms, is necessary. Additionally, workforce issues should be addressed, promoting staff retention and expanding recruitment into new primary care roles. Integration of pharmacy and general practice services can also enhance primary care capabilities. Leveraging information technology and the wider primary care team can enable the delivery of QOF elements at scale, streamlining care processes and improving the efficiency of QOF.

Retaining Essential Elements of QOF

While criticisms exist regarding the QOF’s reporting domains and its evaluation of important dimensions of care quality, it is essential to retain its best elements. This includes focusing on early detection and management of long-term conditions while improving support through information technology and the wider primary care team. Recent research from Scotland demonstrates that the elimination of financial incentives can lead to reductions in recorded quality of care, emphasizing the importance of maintaining an effective QOF program.

Conclusion

The Quality and Outcomes Framework (QOF) remains an integral part of England’s NHS. Despite challenges faced by the healthcare system, the QOF’s role in improving health outcomes, addressing public health challenges, and promoting comprehensive care cannot be overlooked. By adequately supporting primary care teams, addressing workforce issues, and using technology and the wider primary care team, the QOF can continue to play a crucial role in reducing health inequalities and improving health outcomes in England.

Tools for measuring individual self-care capability

Our ability to engage in self-care practices plays a crucial role in promoting overall well-being and preventing and managing non-communicable diseases. To support individuals in assessing their self-care capabilities, many measurement tools have been developed. However, a comprehensive review specifically focusing on non-mono-disease specific self-care measurement tools for adults has been lacking. Our  scoping review in the journal BMC Public Health aims to identify and characterise such tools, including their content, structure, and psychometric properties.

Shifting Emphasis and Methodology

The review encompassed a thorough search of Embase, PubMed, PsycINFO, and CINAHL databases, covering a wide range of MeSH terms and keywords from January 1950 to November 2022. The inclusion criteria involved tools that assess health literacy, capability, and performance of general health self-care practices, targeting adults. Tools exclusive to disease management or specific medical settings were excluded. A total of 38 relevant tools, described in 42 primary reference studies, were identified from a pool of 26,304 reports.

A key observation from the descriptive analysis was the temporal shift in emphasis among the identified tools. Initially, there was a stronger focus on rehabilitation-oriented tools, while more recent tools have shown a shift towards prevention-oriented approaches. This reflects a growing recognition of the importance of proactive self-care practices to maintain optimal health and prevent the onset or progression of diseases.

Additionally, the method of administering these tools has evolved over time. Traditional observe-and-interview style methods have given way to self-reporting tools, which empower individuals to actively participate in assessing their own self-care capabilities. This shift in methods recognizes the value of self-awareness and self-reflection as integral components of self-care.

Content Assessment and Limitations

To provide a qualitative assessment of each tool, the review utilized the Seven Pillars of Self-Care framework. This framework encompasses seven domains of self-care: health literacy, self-awareness of physical and mental well-being, self-management of health conditions, physical activity, healthy eating, risk avoidance or mitigation, and good hygiene practices. Surprisingly, only five out of the identified tools incorporated questions that covered all seven pillars of self-care. This finding highlights the need for the development of a comprehensive, validated, and easily accessible tool capable of assessing a wide range of self-care practices.

While this review makes significant strides in identifying and characterizing non-mono-disease specific self-care measurement tools, it does have limitations. For example, the search was limited to specific databases and only included English-language studies. Therefore, some relevant tools and studies in other languages may have been overlooked.

Implications and Future Directions

The findings of this review underscore the importance of enhancing our understanding and assessment of self-care capabilities. By incorporating the Seven Pillars of Self-Care, a comprehensive tool can provide a holistic assessment, allowing for targeted health and social care interventions. Such interventions can empower individuals to improve their self-care practices, thereby promoting better health outcomes and reducing the burden of chronic diseases.

Moving forwards, future research should focus on developing a comprehensive, validated tool that encompasses a broader range of self-care practices. Additionally, efforts should be made to ensure the accessibility and usability of such a tool, considering diverse populations and their unique needs. Collaborative efforts between researchers, healthcare professionals, and technology experts can facilitate the creation of an effective and widely applicable self-care measurement tool.

Conclusion

Self-care is a fundamental aspect of promoting health and well-being across diverse populations. While several disease specific self-care measurement tools exist, this review highlights the need for a comprehensive, validated, and easily accessible tool that assesses a wide range of self-care practices. By embracing the Seven Pillars of Self-Care framework, we can effectively evaluate individual self-care capabilities, inform targeted interventions, and empower individuals to take an active role in their health and well-being. With continued research and collaboration, we can develop tools that facilitate and support the practice of self-care, ultimately leading to improved health outcomes for individuals and communities alike.

How can we improve the quality of data collected in general practice?

The primary purpose of general practice electronic health records (EHRs) is to help staff deliver patient care. In an article published in the British Medical Journal, Lara Shemtob, Thomas Beaney, John Norton and I discuss the need for the general practice staff entering data in electronic health records to be more connected to those using the information in areas such as healthcare planning, research and quality improvement.

Documentation facilitates continuity of care and allows symptoms to be tracked over time. Most information is entered into the electronic record as unstructured free text, particularly during time pressed consultations. Although free text provides a mostly adequate record of what has taken place in clinical encounters, it is less useful than structured data for NHS management, quality improvement, and research. Furthermore, free text cannot be used to populate problem lists, calculate risk scores, or feed into clinical management prompts in electronic records, all of which facilitate delivery of appropriate care to patients.

Creating high quality structured data that can be used for health service planning, quality improvement, or research requires clinical coding systems that are confusing to many clinicians. For example, coding can seem rigid in ascribing concrete labels to symptoms that may be evolving or of diagnostic uncertainty. It is time consuming for staff to process external inputs to the electronic record, such as letters from secondary care, and if this is done by administrators, comprehension of clinical information may be a further barrier to high quality structured data entry.

The content of digital communications such as text messages from patients to clinicians, emails, and e-consultations may also need to be converted to structured data, even if the communication exists in the electronic health record. This all represents additional work for clinicians with seemingly little direct incentive for patients. As frontline clinical staff are usually not involved in the secondary uses of data, such as health service development and planning, they may not consider the extra work a priority.

To maximise the potential of routinely collected data, we need to connect those entering the data with those using them, also incorporating patients as key beneficiaries. This requires adopting a learning health systems approach to improving health outcomes, which involves patients and clinicians working with researchers to deliver evidence based change, and making better use of existing technology to improve standardised data input while delivering care.

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Data from primary care played a key role in the UK’s Covid-19 pandemic response as shown in this slide which uses data from a range of sources – including general practice records – to examine the impact of vaccination on hospital admissions for Covid-19 in England.

Arguments for and against user fees for NHS primary care in England

There has been considerable recent debate about charging for GP appointments after comments from two former UK health secretaries, Kenneth Clarke and Sajid Javid, elicited strong responses both for and against user fees. Let’s try to put aside ideology and emotion and look objectively at the evidence and arguments around user fees in NHS primary care.

Debates over NHS user fees are not new. In 1951, Hugh Gaitskell introduced charges for prescriptions, spectacles, and dentures. Aneurin Bevan, minister for labour and architect of the NHS, resigned in protest at this abandonment of the principle of NHS care being free at the point of need. Many developed countries already charge users to access primary care services, often through a flat-rate co-payment. However, there is a lack of evidence about the impact of such fees on access to healthcare, health inequalities, and clinical outcomes. A key study on the impact of user fees in a high income country (the RAND Health Insurance Experiment) is now nearly 40 years old.

User fees should theoretically encourage patients to act prudently and so reduce “unnecessary” or “inappropriate” use of healthcare. Some European countries with user fees for primary care have indeed seen lower rates of healthcare utilisation. But this theory is based on the assumption that patients can safely and effectively distinguish between necessary and unnecessary care. In reality, preventive care and chronic disease management are both likely to decline when fees are in place, with patients often delaying presentation until costly medical crises occur.

Expectations about what the UK NHS should offer are already high among the public, and user fees may further increase expectation of a “return on investment.” Doctors may feel pressure to provide prescriptions and referrals, or carry out investigations, to satisfy patients who have paid to see them. User fees may also result in patients hoarding health problems, with clinicians expected to tackle more health concerns in the typical 10-15 minute appointment in general practice. Flat-rate user fees might also introduce a financial barrier to healthcare access for people with a low income, potentially widening health inequalities.

The highest users of primary care, such as women seeking maternity care, and those aged under 5 or over 65 years, are also among the group that would probably be exempt from user fees. If people with a low income are also exempted from fees, we may see little reduction in GP workload, and only modest additional revenues for the NHS—particularly when offset against the costs of collecting fees, including chasing patients for any unpaid fees.

Wealthier patients, when asked to pay for NHS GP appointments, may opt for private primary care instead, further increasing health inequalities and leading to the fragmentation of care. Such an environment could cause private primary care services to expand, increasing shortages of NHS GPs if more GPs choose to work in the private sector.

The collection of user fees would require new billing and debt collection systems across all NHS general practices. To safeguard vulnerable people it would be necessary to create exemptions, which would reduce revenue and further add to administrative costs. After exemptions, user fees would probably only be collected from a relatively small section of the population. For example, around 90% of NHS primary care prescriptions in England are dispensed free of charge and revenues from prescription charges cover only a small percentage of the actual cost of NHS drugs.

User fees may also lead to false economies if they deter people from accessing primary care when they should, resulting in costly delayed diagnoses (for example, for cancer), or lead people to seek care only for acute problems, deprioritising important preventive and chronic care.

User fees will also be ineffective if they divert costs to other parts of the NHS such as accident and emergency departments or urgent care centres. In the USA, for example, user fees have led to “offsetting” of costs, with increased hospital admissions and use of acute mental health services. Patients may therefore choose to use services that are “free” to the user but expensive to the system, such as emergency care. A coherent policy would require simultaneous setting of fees in related areas of the NHS—for example, charging a fee for attending A&E.

UK residents benefit from a high level of financial protection from the costs of illness. Accustomed to free primary care for many decades, the public is likely to resist such fees strongly. As a result, any political party that advocated NHS user fees may pay a high price at a general election.

Valid arguments exist for and against introducing primary care user fees. User fees are promoted by some commentators as a remedy to current NHS challenges in areas such as funding and workload. Yet primary care workload and NHS deficits are also symptoms of deeper problems, such as shortages of clinical staff and reactive, fragmented care. Consequently, user fees by themselves won’t be the solution to problems that have proven intractable for the NHS to solve.

We do, however, need to look at what services we expect NHS general practices to provide and how we fund these services. This will include reviewing the current employment models of NHS GPs. If governments in the UK do not want to fund NHS GP services adequately, user fees of some kind (perhaps for “add-on” but not for core primary care services) or two-tier primary healthcare may be inevitable outcomes.

Source: Azeem Majeed. Let’s look dispassionately at the arguments for and against user fees for NHS primary care in Englandhttps://www.bmj.com/content/380/bmj.p303