The doctor won’t see you now – NHS data needs a health check!

On BBC Radio 4 the other day, I heard that people who have not been to see their local GP in the last 5 years could face being ‘struck-off’ from the register and denied access until they re-register – the story is also covered in most of the national press, including The Guardian. It’s an effort to save money on NHS England’s £9bn annual expenditure on GP practices, but is it the most cost-effective and patient-friendly approach for updating NHS records?

Under the contract, an NHS supplier (Capita) will write every year to all patients who have not been in to see their local doctor or practice nurse in the last five years. This is aimed at removing those who have moved away or died – every name on the register costs the NHS on average around £136 (as at 2013/14) in payments to the GP. After Capita receives the list of names from the GP practice, they’ll send out two letters, the first within ten working days and the next within six months. If they get no reply, the person will be removed from the list. Of course, as well as those who have moved away or died, this will end up removing healthy people who have not seen the GP and don’t respond to either letter. An investigation in 2013 by Pulse, the magazine for GP’s, revealed that “over half of patients removed from practice lists in trials in some areas have been forced to re-register with their practice, with GP’s often blamed for the administrative error. PCTs (Primary Care Trusts) are scrambling to hit the Government’s target of removing 2.5 million patients from practice lists, often targeting the most vulnerable patients, including those with learning disabilities, the very elderly and children.” According to Pulse, the average proportion that were forced to re-register was 9.8%.

This problem of so-called ‘ghost patients’ falsely inflating GP patient lists, and therefore practice incomes, has been an issue for NHS primary care management since at least the 1990’s, and probably long before that. What has almost certainly increased over the last twenty years is the number of temporary residents (e.g. from the rest of the EU) who are very difficult to track.

A spokesperson for the BMA on the radio was quite eloquent on why the NHS scheme was badly flawed, but had no effective answer when the interviewer asked what alternatives there were – that’s what I want to examine here, an analytical approach to a typical Data Quality challenge.

First, what do we know about the current systems? There is a single UK NHS number database, against which all GP practice database registers are automatically reconciled on a regular basis, so that transfers when people move and register with a new GP are well handled. Registered deaths, people imprisoned and those enlisting in the armed forces are also regularly reconciled. Extensive efforts are made to manage common issues such as naming conventions in different cultures, misspelling, etc. but it’s not clear how effective these are.

But if the GP databases are reconciled against the national NHS number database regularly, how is it that according to the Daily Mail “latest figures from the Health and Social Care Information Centre show there are 57.6 million patients registered with a GP in England compared to a population of 55.1 million”? There will be a small proportion of this excess due to inadequacies in matching algorithms or incorrect data being provided, but given that registering a death and registering at a new GP both require provision of the NHS number, any inadequacies here aren’t likely to cause many of the excess registrations. It seems likely that the two major causes are:

  • People who have moved out of the area and not yet registered with a new practice.
  • As mentioned above, temporary residents with NHS numbers that have left the country.

To Data Quality professionals, the obvious solution for the first cause is to use specialist list cleansing software and services to identify people who are known to have moved, using readily available data from Royal Mail, Equifax and other companies. This is how many commercial organisations keep their databases up to date and it is far more targeted than writing to every “ghost patient” at their registered address and relying on them to reply. New addresses can be provided for a large proportion of movers so their letters can be addressed accordingly – if they have moved within the local area, their address should be updated rather than the patient be removed. Using the same methods, Capita can also screen for deaths against third party deceased lists, which will probably pick up more deceased names than the NHS system – simple trials will establish what proportion of patients are tracked to a new address, have moved without the new address being known, or have died.

Next, Capita could target the other category, the potential temporary residents from abroad, by writing to adults whose NHS number was issued in the last (say) 10 years.

The remainder of the list can be further segmented, using the targeted approach that the NHS already uses for screening or immunisation requests: for example, elderly people may have gone to live with other family members or moved into a care home, and young people may be registered at university or be sharing accommodation with friends – letters and other communications can be tailored accordingly to solicit the best response.

What remains after sending targeted letters in each category above probably represents people in a demographic that should still be registered with the practice. Further trials would establish the best approach (in terms of cost and accuracy) for this group: maybe it is cost-effective to write to them and remove non-responders, but if this resulted in only removing a small number, some of these wrongly, maybe it is not worth mailing them.

The bottom line is that well-established Data Quality practices of automatic suppression and change of address, allied with smart targeting, can reduce the costs of the exercise and will make sure that the NHS doesn’t penalise healthy people simply for… being healthy!

The New Paradigm in Healthcare Data Quality

There is no higher importance in managing customer information than when making decisions on health care. While most markets are busy striving for a ‘single customer view’ to improve customer service KPIs or marketing campaign results, healthcare organizations must focus on establishing  a ‘single patient view’, making sure a full patient history is attached to a single, correct contact.  Unlike in traditional CRM solutions, healthcare data is inherently disparate
and is managed by a wide variety of patient systems that, in addition to collecting and managing contact data, also tracks thousands of patient data points including electronic health records, insurance coverage, provider names,  prescriptions and more. Needless to say, establishing the relationships between patients and their healthcare providers, insurers, brokers, pharmacies and the like or even grouping families and couples together, is a significant challenge. Among them are issues with maiden/married last names, migration of individuals between family units and insurance plans, keying errors at point of entry or even deliberate attempts by consumers to defraud the healthcare system.

In many cases, the single patient view can be handled through unique identifiers , such as those for group health plans or for individuals within their provider network. This was an accepted practice at a recent Kaiser Permanente location I visited, where a gentleman went to the counter and reeled off his nine digit patient number before saying “hello”. But while patient ID numbers are standard identifiers, they will differ between suppliers and patients can’t be relied on to use it as their first method of identification. This is where accuracy and access to other collected data points (I.e. SSN, DOB and current address) becomes critical.

While healthcare organizations have done a decent job so far of attempting to establish and utilize this ‘single patient view’, the healthcare data quality paradigm is shifting once again. For example, The Patient Protection and Affordable Care Act (PPACA) means that healthcare organizations will now have to deal with more data, from more sources and face tougher regulations on how to manage and maintain that data.  The ObamaCare Health Insurance Exchange Pool means that more Americans can potentially benefit from health insurance coverage, increasing the number with coverage by around 30 million. Through these new initiatives, consumers will also have greater choice for both coverage and services  – all further distributing the data that desperately needs to be linked.

With such inherent change – how do you effectively service patients at the point-of-care? And, do you want your trained medics and patient management team to be responsible for the data quality audit before such care can even begin?

So what are the new dynamics that healthcare companies need to plan for?

  • Addition of new patients into a system without prior medical coverage or records
  • Frequent movement of consumers between healthcare plans under the choice offered by the affordable care scheme
  • Increased mobility of individuals through healthcare systems as they consume different vendors and services

This increased transactional activity means healthcare data managers must go beyond the existing efforts of linking internal data and start to look at how to share data across systems (both internal and external) and invest in technology that will facilitate this critical information exchange. Granted, this will be a significant challenge given the fact that many organizations have several proprietary systems, contract requirements and privacy concerns but oddly enough, this begins with best practices in managing contact data effectively.

Over the last year, I’ve worked with an increasing number of customers on the issue of managing the introduction of new data into healthcare databases.  Just like healthcare, data quality is both preventative and curative. Curative measures include triage on existing poor quality data, and investigating the latent symptoms of unidentified relationships in the data. The preventative measures are to introduce a regimen of using DQ tools to accurately capture new information at
point of entry efficiently, and to help identify existing customers quickly and accurately.

For healthcare customers, we’ve managed to do just this by implementing helpIT systems’ technology, matchIT SQL to deal with the backend data matching, validation and merging and findIT S2 to empower users to quickly and accurately identify existing patients or validate new patient details with the minimum of keystrokes. This complementary approach gives a huge return on investment allowing clinical end-users to focus on the task at hand, rather than repeatedly dealing with data issues.

Whenever there is movement in data or new sources of information, data quality issues will arise. But when it comes to healthcare data quality, I’m sure healthcare DBA’s and other administrators are fully aware of the stakes at hand. Improving and streamlining data capture plus tapping into the various technology connectors that will give physicians and service providers access to all patient data will have a profound effect on patient care, healthcare costs, physician workloads and access to relevant treatment. Ultimately, this is the desired outcome.

I’m delighted to be engaged further on this subject so if you have more insight to share, please comment on this or drop me a line.