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!

Driving Business with a Real-Time Single Customer View

Since we blogged about the challenges we overcame to deliver a Single Customer View for a major retailer a few years ago, we’ve found a lot of the same challenges repeated across other industry sectors such as non-profit, financial services and education, as well as marketing services providers managing marketing databases for companies in many different sectors. So if that’s more of the same, what’s different? In a word, time. It’s no longer good enough to have a Single Customer View that is only up to date every night, it should be up to date as quickly as your customer switches from one device to another – that is, in real time.

What are the benefits of a real-time Single Customer View?

 

Let’s stick with the multi-channel retail example both for continuity and because increasingly any product can be viewed through the eyes of a shopper, whether it is a scarf, a phone, a take-out meal, an insurance policy or a credit card account. It is widely recognized that the key to success in retail is a positive customer experience, so let’s look at some research findings:

To illustrate, if a customer orders online using their home computer for collection in store, and then after leaving home they want to change the order (using the browser on their phone or by calling the central customer service line), they expect the vendor to have the latest information about the order immediately available – otherwise, the potential for customer disenchantment is spelt out in the JDA research quoted above. If the info is all up to date, the new visit/call from the customer is an opportunity for the vendor to pitch an additional purchase, based on a 360° view of the customer’s account.

So how can you deliver a real time Single Customer View?

 

To answer this question, we first need to review where the moving data that we discussed before is coming from: keeping with the multi-channel retail example, it’s from Point-of-Sale systems in store, customers entering orders on the web site and call center operatives entering and looking up orders. These may be feeding into multiple corporate databases (ERP, Accounts, different subsidiary businesses etc.)  The challenge is: how do we perform the standardization, verification and matching that is required, classify misleading data etc. all on the fly, given that there can be as many as a dozen different data flows to handle? And how do we do all this quickly enough to ensure that the operator always has a current and complete view of the customer?

The key to meeting the challenge posed by the need for a real time Single Customer View is to accept that traditional disk-based database technology is too slow – we can’t afford the time to write a dozen or more transactions to disk, standardize and link all these by writing more records to disk and then read it all back from various disks to give the information to the operator – we can’t expect them to have a coffee break between every transaction!

To us the answer was obvious – all the data needs to be kept in computer memory, updated in memory and read back from memory, so getting away from the limitations placed by conventional hard disks and even solid state disks. But, you may say, that’s fine for small volumes of data but what if we’re streaming thousands of transactions a minute into databases with tens (or even hundreds) of millions of customers? The good news is that computer memory is so cheap these days that it’s extremely cost-effective to provision enough memory to handle even a billion customer accounts, with failover to a mirror of the data in the event of a problem.

Now it’s all very well to say “just use a lot of memory”, but can you find software that will run on all the different varieties of hardware, server technology and database systems that make up the corporate data sets? And will this software allow for the different kinds of error and discrepancy that arise when people enter name, company name, mailing address, email and multiple phone numbers? Even more challenging, will it allow for misleading data such as in store purchases being entered using a store address as the customer address, or a customer giving their partner’s phone number along with their own name and email address?

Once you’ve successfully managed to process the data real-time, you can begin to organize, understand and make use of it in real-time. To use the retail example one final time, now you can take the call from the customer on their way to collect their order and (by finding the order linked to their mobile number) enable them easily to add an item they’ve forgotten plus another item prompted by their purchase history. If the branch near home doesn’t have all the items in stock, you can direct them to the branch which does have the stock near their office – based on an up to date work address linked to the customer. With a real-time, 360° Single Customer View, it’s easy!

Why Customers Must Be More Than Numbers

I read with some amazement a story in the London Daily Telegraph this week about a customer of NatWest Bank who sent £11,200 last month via online banking to an unknown company instead of his wife. Although Paul Sampson had correctly entered his wife’s name, sort code and account number when he first made an online payment to her HSBC account, he wasn’t aware that she had subsequently closed the account.

Mr Sampson thought he was transferring £11,200 to his wife: he clicked Margaret’s name among a list of payees saved in his NatWest banking profile and confirmed the transaction, but the payment went to a business in Leeds. Mr Sampson believes that HSBC had reissued his wife’s old account number to someone else, a company whose name they refused to tell him. NatWest told Mr Sampson it was powerless to claw the money back.

HSBC said it had contacted its customer, but it had no obligation regarding the money. HSBC insisted that the account number in question was not “recycled”, saying Mr Sampson must have made a typing error when he first saved the details, which he disputes. Although the money was in fact returned after the newspaper contacted HSBC, a very large issue has not been resolved.

Although news to most of us, it is apparently a common practice among banks in the UK to recycle account numbers, presumably because banking systems are so entrenched around 8 or 9 digit account numbers that they are concerned about running out of numbers. Apparently a recent code of practice suggests that banks should warn the customer making the payment if they haven’t sent money to this payee for 13 months, but according to the Daily Telegraph “No major high street bank could confirm that it followed this part of the code”.

The Daily Telegraph goes on to state that the recipients of electronic payments are identified by account numbers only. The names are not checked in the process, so even if they do not match, the transaction can proceed. “This is now a major issue when you can use something as basic as a mobile phone number to transfer money,” said Mike Pemberton, of solicitors Stephensons. “If you get one digit wrong there’s no other backup check, like a person’s name – once it’s gone it’s gone.” If you misdirect an online payment, your bank should contact the other bank within two working days of your having informed them of the error, but they have no legal obligation to help.

Mr Sampson obviously expected that the bank’s software would check that the account number belonged to the account name he had stored in his online payee list, but apparently UK banking software doesn’t do this. Why on earth not? Surely it’s not unreasonable for banks with all the money they spend on computer systems to perform this safety check? It’s not good enough to point to the problems that can arise when a name is entered in different ways such as Sheila Jones, Mrs S Jones, Sheila M Jones, SM Jones, Mrs S M Jones, Mrs Sheila Mary Jones etc.

These are all elementary examples for intelligent name matching software.  More challenging are typos, nicknames and other inconsistencies such as those caused by poor handwriting, which would all occur regularly should banks check the name belonging to the account number. But software such as matchIT Hub is easily available to cope with these challenges too, as well as the even more challenging job of matching joint names and business names.

There are also issues in the USA with banking software matching names – I remember when I first wanted to transfer money from my Chase account to my Citibank account, I could only do so if the two accounts had exactly the same name – these were joint accounts and the names had to match exactly letter for letter, so I had to either change the name on one of the accounts or open a new one! Having been an enthusiastic user of the system in the USA for sending money to someone electronically using just their email address, I’m now starting to worry about the wisdom of this…

We banking customers should perhaps question our banks more closely about the checks that they employ when we make online payments!

helpIT Systems is Driving Data Quality

For most of us around the US, the Department of Motor Vehicles is a dreaded place, bringing with it a reputation of long lines, mountains of paperwork and drawn out processes. As customers, we loathe the trip to the DMV and though while standing in line, we may not give it much thought  – the reality is, poor data quality is a common culprit of some of these DMV woes. While it may seem unlikely that an organization as large and bureaucratic as the DMV can right the ship, today, DMV’s around the country are fighting back with calculated investments in data quality.

While improving the quality of registered driver data is not a new concept, technology systems implemented 15-20 years ago have long been a barrier for DMVs to actually take corrective action. However, as more DMVs begin to modernize their IT infrastructure, data quality projects are becoming more of a reality. Over the past year, helpIT has begun work with several DMVs to implement solutions designed to cleanse driver data, eliminate duplicate records, update addresses and even improve the quality of incoming data.

From a batch perspective, setting up a solution to cleanse the existing database paves the way for DMVs to implement other types of operational efficiencies like putting the license renewal process online, offering email notification of specific deadlines and reducing the waste associated with having (and trying to work with) bad data.

In addition to cleaning up existing state databases, some DMVs are taking the initiative a step further and working with helpIT to take more proactive measures by incorporating real-time address validation into their systems.  This ‘real-time data quality’ creates a firewall of sorts, facilitating the capture of accurate data by DMV representatives – while you provide it (via phone or at a window). With typedown technology embedded directly within DMV data entry forms, if there is a problem with your address, or you accidently forgot to provide them with information that affects the accuracy, like your apartment number or a street directional (North vs. South), the representatives are empowered to prompt and request clarification.

Getting your contact data to be accurate from the start means your new license is provided immediately without you having to make another visit, or call and wait on hold for 30 minutes just to resolve the problem that could have been no more than a simple typo.

Having met several DMV employees over the past year, it’s obvious that they want you to have an excellent experience. Better data quality is a great place to start. Even while DMV budgets are slashed year after year, modest investments in data quality software are yielding big results in customer experience.

 

If you want to learn more about improving the quality of your data, contact us at 866.332.7132 for a free demo of our comprehensive suite of data quality products.