Preferred Provider Networks (PPNs)

Public or private health care insurance providers or other third-party payers establish Preferred Provider Networks (PPNs), sometimes referred to as Preferred Provider Organizations (PPOs), to connect patients to health care professionals who satisfy specific criteria.

The purpose of a PPN may be to efficiently connect patients to specialized care providers, to drive the value of care and improve outcomes and/or to contain health care costs.

According to the Financial Services Regulatory Authority (FSRA, formerly FSCO) PPNs may be characterized by one or more of the following:

• A contractual relationship between an insurer or other third-party payor and a health care professional, often including pre-arranged prices for services
• An arrangement whereby patient participation in a PPN is initiated following an insurer or other third-party payor’s referral
• An arrangement whereby the insurer or other third-party payor may require Pre-approval for treatments provided through a PPN, and the payor may or may not set a cap on the cost per treatment and/or the amount of treatment to be provided.^[1]

PPN Membership

Depending on the goals of the PPN, membership within it may be determined according to different processes or criteria. For example, membership in a PPN may be determined through a competitive request for proposals (RFPs) in which practitioners must demonstrate their ability to respond to the care delivery needs and goals of the funder.

PPNs may also be specific to certain employers or benefits plans to drive cost savings and/or value enhancements. For example, an employer may establish a network of preferred pharmaceutical providers and use criteria such as patient adherence to medications or pharmaceutical dispensing fees as a condition of preferred status.

Other PPNs, such as those sponsored by extended health care (EHC) insurers and based on paid memberships, may be less specialized and/or have less stringent requirements for entry and function essentially as third-party marketing services for health care professionals.

The goal of these PPNs is to connect patients to a broad spectrum and diversity of providers by generating large, geocoded databases of health services providers and offering this information in a centralized searchable portal. In doing so, they also afford the opportunity to collect data on all those who interact with the PPN.

Things to consider before joining a paid membership PPN: 

When assessing the risks and benefits associated with signing up, we strongly encourage the following:

• Understand the purpose of the PPN and assess the extent to which participation within it aligns with the goals and values of your practice
• Read the details of the PPN contract carefully before signing and keep a copy of the contract on hand for future reference in case any issues arise
• Keep good records of both the quantity and quality of the business that participation in the PPN generates for your practice. This will enable you to conduct an accurate cost-benefit analysis when the time comes to renew your membership

In the paid membership model, you may initially be offered the option to join for free or at a discounted rate. After a specified period, you will then be asked to pay a monthly or yearly fee to maintain your status as “preferred” and continue to be listed at the top of the search hierarchy. However, if you opt not to pay to belong to one or more such services, you may lose your status within the search hierarchy and your name will no longer appear in top results for patients searching within that PPN.

A PPN may be either “open” or “closed.” In an open network, your patients may choose a provider from outside of the network. However, the expenses reimbursed may be less than if your patient chose an in-network provider. If a network is closed, then your patient must use an in-network provider for their expenses to be eligible for reimbursement.

According to one benefits consultant, characteristics of successful EHC PPN structures include:

• A focus on preserving benefits levels for members while looking towards plan sustainability in the face of rising costs
• An emphasis on positive health outcomes as a return on investment in the health of employees
• A desire to add value rather than a punitive cost-containment tool
• An effort to develop true partnerships where everyone wins: the recipient of care, the provider of care and the insurer^[2]

Cost Containment

Efforts by employers and insurers to contain rising costs are among the biggest trends identified in recent years by third-party surveys. While the growth in costs is a complex issue, some key drivers identified by the 2020 Sanofi Canada Health Care Survey are: the cost of pharmaceuticals, growth in the use of Paramedical services and Fraud. These results echo previous years, except fraud is new as of 2020.

While some insurers recognize the benefits of neuromusculoskeletal (nMSK) care in illness prevention and chronic disease management, others have tended to view drug costs and paramedical benefits in more zero-sum terms. For example, some plan designs may feature a trade-off between unlimited drug coverage and other forms of coverage.

Employers and insurers pursue diverse methods of cost containment, including:

• Drug caps that set a maximum amount on what employees can spend on drugs
• Co-payments and deductibles to discourage what is perceived as unnecessary or frivolous use of benefits
• The use of Pay for performance metrics, which tie reimbursement to outcomes

While some of these methods, such as co-payments, have proven effective in containing costs, the efficacy of others, such as pay for performance, are unclear.^[3]

Another response to growing costs, which has been gaining in popularity, is Employee Life and Health Trusts (ELHTs). ELHTs are like health and welfare trusts and are scheduled to replace them completely by 2021. Health and welfare trusts allow for the provision of health care benefits “through third-party insurance contracts (an insured plan), directly from the property of the health and welfare trust (a self-insured plan), or through a combination of both.”^[4]

An ELHT may involve a single employer, or a group of employers. ELHTs can be an attractive option to employers because, unlike the volatility of insurance premiums, they enable the employer to make a regular, predictable “defined” contribution to the trust. Benefits in an ELHT are pre-funded. Since ELHTs must be managed by an independent board of trustees, employers are able to divest themselves of risks associated with plan sponsorship.

Big Data, Machine learning and Artificial Intelligence (AI)

These technologies are transforming health care and insurance industries in profound ways with far-reaching implications. AI applications are streamlining the ways insurers approach issues of risk and pricing, how they engage in sales and marketing, manage benefits claims and the techniques they use to prevent and detect Benefits fraud.

Big Data and AI

Big data refers to large, complex data that cannot be analyzed using traditional methods. AI encompasses a broad range of technologies that enables systems to draw on big data, statistics and probability analyses. AI systems use these tools to mimic human cognition and produce outcomes that appear “intelligent” to human observers.

It is different than conventional computer programming in that instead of top-down programming, it uses the combination of vast data and relatively simple algorithms to support a process known as machine learning – the ability of a machine to change its behaviour based on the accumulation of new data or ‘experience.’

AI in Fraud Detection and Prevention

Traditionally, computer programs analyzed health insurance claims using preprogrammed red flags, or “fraudulent indicators.” In the past, fraudulent claims had to fit into a set template to be recognized. Advances in AI and machine learning allow systems to process extremely large volumes of data very quickly. They also allow systems to learn from data on their own. This means AI systems can detect novel patterns in claims data and develop or refine criteria for selecting cases as “unusual” and in need of further investigation.

Machine Learning

Machine learning includes a range of different learning techniques, such as supervised, unsupervised, and deep learning.

Algorithms associated with machine learning techniques allow systems to perform many complex tasks, including:

• Autonomously control machines like driverless cars
• Make predictions about things like consumer preferences
• Support health care professionals in clinical decision-making by incorporating previous data on similar diagnoses, practices and Guidelines, and medication history

Machine learning is further used in fraud detection to filter and prioritize the cases that are likely to result in a successful resolution for the insurer. For example, to identify cases where there is a high likelihood that a claim was paid in error, or where a finding of fraud can be substantiated. AI-driven systems can also make suggestions to an auditor with respect to the grounds on which a claim could be denied. This, in turn, frees up administrators to investigate flagged claims more fully.^[5]

The numerous avenues in which data can be collected – from wearables to smart home technology – have allowed AI applications to use their machine learning and data processing capabilities to generate detailed customer profiles. These profiles are used to match potential customers with products and services. If you have ever received a targeted social media advertisement about a product you are thinking of buying – or have perhaps already purchased! – this may be an AI-powered match.

Big Data and PPNs

In developing the PPNs discussed previously, insurance companies are both providing a service and developing a growing pool of data. Social media platforms tend to gather data for the purposes of selling it to advertisers to generate targeted ads. While insurers have been able to gather vast quantities of consumer data through the course of ordinary business practices that are increasingly digital, they have often found this internally generated data to be insufficiently robust for the purposes of training machine learning algorithms. The external data generated by social media, company loyalty programs and content marketing creates massive pools that insurers can purchase or draw upon to supplement their own data.^[6]

Insurance profits depend on, among other things, the extent to which future risks or usage patterns can accurately be predicted based on historical data. Detailed consumer profiles are valuable to insurers, as they can use AI to detect patterns in a wide range of health-related behaviours across different demographic or geographic profiles and time periods. This data can be used to inform risk assessment or the development of new insurance products and services.

Emerging AI Applications

Insurance companies use data to conduct research to understand and make predictions about new and evolving trends in insurance claims. For example, responding to the growth in mental health-related long-term disability (LTD) claims, Janssen Inc. analyzed the correlations across claimant level prescription drug, EHC and long-term disability data for 125,000 individuals during a three-year period. This analysis showed that employees who had two or more treatment failures cost on average three times more than employees with less than two treatment failures.^[7]

This study did not have access to information about patient diagnoses or to clinical analyses of therapeutic outcomes. Rather, both illness and efficacy of treatment were inferred from the data. A treatment failure was defined as any instance where a patient switched between medications “that are typically prescribed” for Major Depressive Disorder (MDD).

Enhancing Care

Other uses of AI in health insurance continue to be identified and developed.

For example, management consulting firm McKinsey & Company observes:

Initial use cases have been found for AI-supported systems that enhance care—for instance, in the development of customized offers for patients suffering from chronic diseases or for identifying clinical pathways that fail to adhere to guidelines.^[8]

In terms of matching your patients with customized offers, we see what this may look like through our discussion of PPNs.

Where AI is used to identify common clinical pathways and detect deviance from these or from other evidence-based pathways, the implications for you may be far-reaching. As the New Scientist, observes, “Once a network is up and running, not even its creators can know what it is doing – a largely unforeseen problem that, as AI assumes ever more decision-making powers within computer systems, researchers are increasingly having to grapple with.”^[9]

We continue to be actively engaged in the research and development of initiatives that support you and as an effective, and transparent partner supporting patients. For example, we partnered with the Centre for Effective Practice to develop the Manual Therapy as an Evidence-Based Referral for Musculoskeletal Pain Tool to present manual therapy, including chiropractic care, as an evidence-based alternative treatment to opioids.