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.


When Direct Marketing Fails…

As a Direct Marketer in a tough economy, you are constantly being asked to do more with less.
Generate more leads. Improve campaign results. Increase brand awareness. Streamline operational efficiency. Unfortunately, poor quality customer and prospect data can sabotage your direct marketing efforts and your reputation in the process.

Want to get a handle on the ways that data could be tripping you up? Here’s a quick look at the top 5 Ways Bad Data is Hurting Your Direct Marketing Efforts AND how you can start to turn that trend around…

1. BAD DATA Wastes Money

In this most obvious example, companies with large, unkempt databases ‘pay’ for bad data, directly and indirectly. How much? Duplicate records mean duplicate print costs and double the postage but half the potential return while invalid or outdated addresses and garbage data is like throwing money out the window. Even in the digital world, bigger databases cost more money. Email vendors, marketing automation vendors and database marketing companies typically charge by volume of records – so larger, unclean databases will cost you more regardless of your marketing strategies.

2. BAD DATA Reduces Response Rate

While bad data has the potential to waste significant dollars per campaign, the flip side of that coin means your campaign response rates are compromised. In other words, when you send that direct mail campaign out, even if you get tons of responses – they are always divided by the ‘potential’ of your entire database (response/total sent). As a direct marketer (whether you use email, direct mail or telemarketing) the only way to demonstrate the maximum return on investment is to remove duplicate records, update old addresses and eliminate anyone who shouldn’t receive your marketing (which could include unsubscribers, gone aways or deceased records).  Now that return rate is an accurate reflection of your skill as a marketer and you wasted a lot less money in getting there.

3. BAD DATA Means Bad Targeting

How well do you know your customers and prospects? How many unique customers are in your CRM? Do you know how much they spend? What they like to buy? Where they shop? If you don’t – it means your direct marketing initiatives are being held back by a lack of information.  The remedy? Put all your customer data in one place, accurately entered and complete. This might mean merging contact data with household income. It might mean linking all their product purchases so you can determine buying habits. It might mean knowing ALL their addresses but choosing a master file so you know how and where to target them appropriately. For retailers this is known as a Single Customer View and it allows you to simply know more about your customer so you can use it to maximize your direct marketing efforts.

4. BAD DATA Hurts Your Reputation

Companies that market well have a better reputation. This needs no data to support it – simply check your kitchen counter for the catalogs and marketing pieces you’ve ‘saved’. What do they have in common? They were all mailed accurately (they made it to your mailbox) and they promote products that interest you (as compared to that motorcycle catalogue you may have tossed). And they showed up at the right time (like that home improvement coupon that arrived just after you purchased your new house). So to protect your marketing reputation you want to suppress and organize your data so that you don’t mail to the wrong people at the wrong time with the wrong message. Remove gone aways, deceased and anyone who does not want to get your mail. Your customers will thank you in the long run by continuing to purchase from your business (and not writing nasty things about you on Twitter). In addition, your ‘green’ credentials will soar by taking the initiative to reduce wasted mail.

5. BAD DATA Risks Opportunities

Almost everyone knows that Marketing and Sales go hand in hand and it’s all related to good data. When sales reps and account people have the correct information on the screen, they can go the distance in selling and up selling to prospects and customers. They can waste less time calling people that are not relevant and spend more time focusing on the ones who are. They can spend less time on each call searching for data or recollecting it and instead, wow the customer with how organized and efficient their interaction is. Accurate and complete data improves sales and wastes far less opportunities.

Mounting evidence suggests that cleaner, more accurate data has a direct impact on your direct marketing success and each year, more and more companies make data quality a key focus in both their marketing and technology departments.  Removing duplicates, updating addresses, gone away/deceased suppression and generating that all-important single customer view will allow you to

So what’s a Direct Marketer to do? Make every effort to regularly dedupe your database or CRM to keep the size reasonable and reduce waste across all your marketing initiatives. It’s also advisable to validate addresses and suppress gone aways and deceased records before a major mail campaign to reduce the waste associated with excess mailers and postage.  To take your Direct Marketing to the next level, work with your technology or database team to establish a comprehensive Single Customer View. This will allow you to use your existing customer data to drive better and more targeted Direct Marketing campaigns. You can also supplement existing data with a wide range of additional datasets. Lastly, keep good track of customers and prospects who unsubscribe or who don’t respond to specific types of mailings so you can market to only those receptive to your offer.

If you are interested in learning about how helpIT systems data quality toolset can address some of your Direct Marketing challenges, please feel free to contact us for a Free Consultation and Trial of our matchIT data cleansing software.