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The NetMinder Blog

4 Ways to Measure Network Strength

Posted by Susan Donegan on Thu, Sep 14, 2017

The health insurance industry has developed a spectrum of network analysis tools to demonstrate a network’s breadth and depth, and to differentiate between networks.There are 4 common methods of network analysis widely used to evaluate health-related insurance products today. We visualize this spectrum as a pyramid to show how frequently the analysis is used and how specific the information is to each company. As you ascend the pyramid the frequency of availability decreases but the knowledge gained becomes more specific and as a result is more valuable to the overall assessment of the networks under consideration.4 ways to measure network strength.jpg

For example, at the bottom of the pyramid, measuring network size is fairly easy and is used in almost every analysis; it’s not very specific to a particular client or prospect. At the top of the pyramid, re-pricing the claims of the incumbent carrier is more difficult to do because it requires more data and cooperation from the prospect and the incumbent, therefore it’s done less frequently. However, when done, it’s very specific to the prospect’s situation. 

Download our whitepaper, The Network Analysis Pyramid for an overview of the most widely used methods to analyze provider networks.

Tags: compare networks, health insurance, network comparison tool, data analysis, network data, provider networks, repricing analysis

Network Analysis with Disruption Reporting

Posted by Susan Donegan on Fri, May 19, 2017

Network_Analysis_Pyramid_Cover.pngThis method of network analysis correlates historical provider utilization and claims experience for a group of employees to the providers in a different network. If you assume that a population will utilize the same set of providers at the same frequency, you can estimate the amount of future utilization that will be in-network. Disruption Reporting is also used as a predictor of future financial experience, with more in-network claims (at a discount) resulting in lower overall claims expenses.

The utilization or claim file required for this method of analysis must include demographic data for each utilized provider, to determine if that provider is in the prospective network. Ideally it would also include quantitative statistics on how much treatment each provider performed, such as:

  • Submitted claims (number and/or amount)
  • Paid claims (number and/or amount)
  • Number of procedures performed
  • Number of patients treated

This type of utilization or claim file is generally only available when the company requesting it has at least 200 employees enrolled in that benefit plan.

Considerations

There is tremendous variation in the format and quality of the utilization and/or claim data files that are included with requests for disruption reports. Unfortunately, it is very common for key provider identifiers to be omitted:  

  • Tax Identification Number (TIN)
  •  National Provider Identifier (NPI)  
  • State license numbers 

In addition to the variation in the utilization and claim data files, there is a tremendous amount of variation in the matching criteria used when these reports are produced. When some networks use looser criteria than others, employers and employees don’t get a clear picture of network access.

Download our whitepaper, The Network Analysis Pyramid for an overview of the most widely used methods to analyze provider networks.

Tags: disruption reporting, data analysis, provider networks, claims data, repricing analysis, discounted fees, network analysis

The Role of Repricing Analysis

Posted by Susan Donegan on Thu, May 11, 2017

Network_Analysis_Pyramid_Cover.pngRepricing Analysis is the least frequently available network analysis method by a wide margin, but when it is available, it is a very good predictor of future financial experience. Repricing Analysis begins with Disruption Reporting, but goes a step further. Once an in-network provider that matches a record in the claim file is identified, the discounted fee arrangement under which that provider is contracted is applied. For out-of-network providers, the reasonable and customary (R&C) charges for the area are applied. Reports from different networks are compared based on the overall cost of the claims for all providers (in-network and out-of-network) and the amount of savings each network would achieve.

Considerations

Because Repricing Analysis is an extension of Disruption Reporting, all of the considerations that affect Disruption Reporting affect Repricing Analysis in the same way.

In addition, Repricing Analysis requires an extremely detailed claim file. In order to apply the discounted fees, the file must include a record for each procedure performed by each provider.

Are you using repricing analysis to help win new business and save retention threats? 

Download our whitepaper, The Network Analysis Pyramid for an overview of the most widely used methods to analyze provider networks.

Tags: disruption reporting, data analysis, provider networks, repricing analysis, discounted fees

Accurate Provider Directories Make Network Comparisons Easier and More Compelling

Posted by Laura McMullen on Mon, Oct 17, 2016

Provider directory accuracy is an important topic for consumers, providers, and payors. It’s obviously very important to us, too – in fact, nearly half of our employees are focused on data accuracy and integrity every day.healthcare_directory.jpg

That’s why we’re following the progress of federal and state regulatory initiatives around ensuring accuracy very closely. I came across an article from Medicare Advantage News (login required) recently that summarizes official remarks at the Medicare Advantage and Prescription Drug Plan fall conference discussing the pilot program conducted by CMS’ Medicare Drug & Health Plan Contract Administration Group to assess directory accuracy. In this initial program, they found:

  • Nearly half (46%) of locations had errors meaning that at least one data element in the directory for that location was inaccurate. Most of the organizations reviewed were 20%-60% inaccurate.
  • Two-thirds of the deficiencies involved listing providers at locations where they don’t practice.
  • Ten percent of the deficiencies were incorrect phone numbers.
  • Twelve percent of the deficiencies were incorrect addresses, including incorrect suite numbers.

The study focused on large organizations with multiple locations and many providers, ultimately contacting nearly 6,000 primary care physicians, oncologists, ophthalmologists, and cardiologists at 11,646 locations. Based on our experience with directory data, inaccurate data is more likely with large practices that include many providers and locations. Take a look at our whitepaper about overstated access in dental directories for more on this subject.

Inaccurate provider directories frustrate members and providers and they also make it hard to compare your network to your competitors’ networks. Here are five best practices for managing your network data that will help you find inaccuracies in your network and make your network stand out:

  1. Review your directory data regularly. Be sure that provider names, addresses, and phone numbers are up to date. Transparency in your reporting will be to your advantage in the long run as it increases member and provider satisfaction by making your directory more reliable.
  2. Check for duplicate records that can be consolidated, especially if you are stacking networks, since it can be hard to identify providers from the vendor network that are already in the carrier network. This will also help streamline your directory validation programs.
  3. Adopt data standardization practices, particularly for numeric fields. For example, make sure leading zeroes on ZIP codes have not been dropped and replaced by the first digit of the ZIP+4. This is common in ZIP codes in New England, New Jersey, and US Caribbean territories. Cleaner data is easier to manage and compare.
  4. Consider including competitor network data in your analyses so that you understand your competitive position, predict results, and prepare for the future.
  5. For Disruption Reporting and Repricing, make sure that provider name data is properly parsed and address data is standardized. Use the same processes for claim and provider data files to give best chance of identifying valid matches.

How are you validating the provider demographic information in your online directory?

Tags: data analysis, network data, network comparisons, provider directories, directory accuracy

10 Key Data Points for Conclusive Network Comparisons

Posted by Laura McMullen on Fri, Jul 15, 2016

Critical Capabilities for Better Network Comparisons

ten_key_data_points.jpgProduct, sales and network teams’ needs are deeply intertwined — and success for each team relies on the ability to find the edge against competitor provider networks. When high-level comparisons suggest networks are the same or no advantage appears, that’s your cue to dig deeper.

Based on our work with more than 50 healthcare companies (and their aggregate 4,500 users), we offer 10 metrics that are critical for making more effective comparisons — which ultimately means designing better networks and selling more effectively against your competition.

The “Must Have” Capabilities for the Three Major Counting Methods

When you compare networks, how you count really matters. Different comparisons return different results, and are useful for different purposes. Selecting the right comparison method is key to your network development strategy, and to helping clients make better decisions.

In most cases, you’re counting by access points, unique providers and unique locations. Take a look at our whitepaper, How You Count Matters as Much as What You Count, for tips about choosing the right counting method for your analysis.  

10 Key Data Points You Must Have

Let’s add an overlay to the capabilities that drive the three effective counting methods — 10 metrics that must be pinpointed for your network development and sales efforts to make meaningful comparisons:

  1. Which network has more unique providers?
  2. Which network has more access points?
  3. How truly similar are the networks you compared? (In other words, how many providers are in both networks?)
  4. How many of each specialty category does each network have?
  5. Based on member demographics, which specialties are most important in this situation? Which network has more?
  6. How many providers in each network are within an X-mile radius of the locations where your group lives and works? Your radius might be smaller for urban ZIP codes and larger for rural ZIPs.
  7. How many locations/provider are in each network? When this ratio is high, directory inflation could be present.
  8. How many providers/location are in each network? When this ratio is high, large practice negotiations could disrupt the network.
  9. Has the network grown or shrunk overall during the last year or six months?
  10. What type of recruiting activity has there been recently? Adding new providers or replacing providers?

If you cannot decisively answer the 10 questions above, you may be missing key opportunities in network development or sales. You would not be alone in this regard: many competitive network data providers exist, but most provide cosmetic ease of use at the cost of more flexible and powerful reporting options.

If you find your teams hamstrung in their quest to make more effective comparisons — and ultimately drive more profitable activity at every level of your organization — click here and get in touch with us. We’ll talk about a better way to find and capitalize on critical points of difference.

Tags: compare networks, data analysis, provider networks, network comparisons, network development

Including employee census data in your network analyses

Posted by Laura McMullen on Thu, Jan 15, 2015

Employee censuses are the heart of the group insurance business. Sales and underwriting teams use them during the sales process, enrollment portal credentials are established using them, and billing and eligibility files are subsets of these lists. Another common use is network accessibility analyses to determine how many providers are within a standard distance.

To take accessibility analyses a little farther, consider including NetMinder reports in the underwriting process to see where you have network advantages and disadvantages for a specific employee population. When you are a finalist for a group, there’s usually only one or two other competitors to evaluate. For some cases though, it’s worth it to compare to a larger group of competitors early in the process to give your team the best possible chance of winning.

To make this analysis easier, we recently added the capability to run NetMinder reports using your client’s employee census to select the geographic area you want to analyze.

Upload your file using the UPLOAD CUSTOM CENSUS option in the geographic scope selection box and make the rest of your choices as usual to get started.

custom_zip_1

Custom_Zip_2

 

 

 

 

 

 

Using custom census geographies has these benefits:

  • Your network comparison will include all of your client’s key areas and match up easily with other analyses.
  • All of the ZIP codes in the file will be included in your report – even if they are not all in the same state.
  • You can use custom census geographies with any NetMinder report – summary or detail. The report will return counts or details for the networks and specialties you select in the counties that contain the ZIP codes in the census file. For example, if 33433 and 33313 are in your census, the report will show Florida as the state and results in Palm Beach and Broward counties.

How do you match employee censuses with competitive network data?

Tags: market comparison, data analysis, insurance companies, network comparisons, ZIP codes and cities, Custom Geographies, NetMinder new features

Five Best Practices to Find the Right Provider Network for Your Customers

Posted by Laura McMullen on Thu, Jul 24, 2014

In a recent blog post entitled, Five Best Practices to Use Network Data and To Grow Your Business, we wrote about the ways carriers and network leasing companies can improve their position in network comparisons by better cleaning their data. Another point of view is from the brokers and consultants who use network analyses to help their customers choose the right benefit plans.

health insurance plansShopping for employee benefits is complicated and time consuming. Employers and other plan sponsors typically rely on a broker or consultant to help them through it. Brokers and consultants know that network issues can turn a satisfied customer into one that goes out to bid in the blink of an eye. Even if everything else is right: price, benefits, service, and timely and accurate claims payments can’t outweigh a network that doesn’t fit the employee population.

As we pointed out in our other post, the best networks:

  • Offer a wide range of choices: multiple general and specialty providers are included in the network
  • Are convenient to use: providers are located near home or work
  • Include popular providers and facilities: providers are the ones that members and their families want to use
  • Save employers and employees money: in-network providers offer meaningful discounts that reduce out-of-pocket expenses and claim costs

Depending on the number of employees your customers have, different types of network analyses are probably available from your carrier partners. Generally, we see four types of network analysis:

  • Network Counting – measure the quantity of providers in each network (available for groups of all sizes)
  • Accessibility Analysis – correlate network provider locations to employee home and work locations (available for groups of all sizes)
  • Disruption Reporting – match historical provider utilization and claims experience for a group to the providers in a different network (available for groups with at least 200 employees)
  • Repricing – compare cost of claims for all providers (in- and out-of-network) if a different network were in place to the cost experienced in the current network (available for very large or self-funded groups)

Download our whitepaper, The Network Analysis Pyramid, to learn more about each method.

As the primary users of network analyses, brokers and consultants are in a unique position to influence the requirements of each type of analysis. Keep these five best practices in mind as you work with carriers on your customers’ behalf:

  1. Insist on clean, accurate data so you get clean, accurate reports.
  2. Clearly identify required fields and formats in all file requests.
  3. Obtain claim data from the incumbent carrier whenever possible.
  4. Choose a consistent counting method for all reports to ensure that you are comparing apples to apples.
  5. Evaluate key specialties separately from the overall network based on your client’s needs.

With all of the changes from the Affordable Care Act, employers and other plan sponsors are relying on brokers and consultants more than ever.

How do you evaluate networks today?


 

Tags: compare networks, data management, market comparison, network metrics, dental network, network providers, health insurance, Affordable Care Act, network comparison tool, disruption reporting, data analysis, network change, Healthcare, healthcare reform, health reform, ACA, healthcare exchanges, provider networks, health insurers

When Is a City Not a City?

Posted by Laura McMullen on Wed, Mar 26, 2014

One of the most common questions we get from NetMinder users when they run reports comparing provider networks in specific cities is why aren’t there any providers in ______________? (Fill in city name here.)

Often the reason is that the client’s definition of a city is different than the preferred US Postal Service definition. That’s why, to ensure you always get apples-to-apples comparisons, we base the city designations in NetMinder on the USPS preferred city designation. 

  • Cleveland is a good example. There are a number of ZIP codes in Cleveland that have both preferred and acceptable city names. See list below:

Cleveland zips resized 600

  • Here’s a screen capture from www.usps.com for zip code 44118 for an example of preferred and acceptable city names.

Cleveland preferred

So, what can you do when you’re running a NetMinder report to avoid this problem? The best thing to do is to run your report at the ZIP code or MSA level. Those geographic areas are more firmly agreed upon than city names. MSA, or Metropolitan Statistical Area, reports can be helpful in this situation because city names are included in the name of the MSA, i.e. CLEVELAND-ELYRIA, OH. If you choose to run a county report, I recommend including city and 5-digit ZIP subtotals to make sure that you include all of the areas you need since county names might not be as familiar as city names.

Try this tip out when you log into NetMinder next time and let us know how it goes in the NetMinder User Group on LinkedIn.

 

Tags: compare networks, data management, business intelligence, data analysis, provider counts, ZIP codes and cities

Overwhelmed by big data?

Posted by Aaron Groffman on Tue, Jan 22, 2013

Netminder powerfuldataBig data was a hot topic for businesses in 2012, and according to a recent Forbes article, it will be even hotter this year when consumers really start to see its impact in everything from retail to healthcare.

The already vast volumes of information that exist online continue to grow every day, presenting enormous opportunities for businesses. Collecting and analyzing these large data sets, aka big data, can help companies make better decisions, predict probable outcomes, cut costs, identify new business opportunities, close more deals and retain more clients.

But how can businesses access the immense amount of information that is available and harness it for effective business intelligence?

Businesses, especially smaller companies, encounter three main challenges when trying to tackle big data, according to a recent article in Inc. First, many don’t have access to big data. Second, they may not have the technology and relational databases needed to easily pull the data. Third, many businesses don’t have the expertise on staff to analyze the data (e.g. a data scientist and a team of analysts).

David Selinger, a founder, CEO and data scientist at RichRelevance who formerly managed data mining and site optimization at Amazon.com, offered some tips for handling big data in a recent article. He suggests that businesses find experts to help them manage and analyze data, take advantage of the cloud, protect privacy, and of course, execute, test and measure.

Do you have experts lined up to take advantage of your big data opportunities?

Tags: data management, business intelligence, big data, data analysis

 

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