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Healthcare Risk Analytics   Power Of Knowledge   Us Captive
Healthcare Risk Analytics   Power Of Knowledge   Us Captive
Healthcare Risk Analytics   Power Of Knowledge   Us Captive
Healthcare Risk Analytics   Power Of Knowledge   Us Captive
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Healthcare Risk Analytics Power Of Knowledge Us Captive

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PointRight Risk Analytics - The Power of Knowledge - published in USCaptive Magazine

PointRight Risk Analytics - The Power of Knowledge - published in USCaptive Magazine

Published in: Economy & Finance, Business
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  • 1. Mary Chmielowiec Executive vice president of insurance, PointRight Inc. Paul Marshall Director of insurance business development, PointRight Inc. ©iStockphoto.com/ H-Gall The power of knowledge Using modern quantitative methodologies for examining data to help draw more accurate conclusions about your risk information is the future, say Mary Chmielowiec and Paul Marshall. US Captive . April 2009
  • 2. Abandoned by traditional insurance carriers during the liability crisis of the late 1990s, many nursing home operators and long-term care providers implemented risk retention strategies, seeking safe harbour from an increasingly erratic and unpredictable insurance marketplace. “Some captives may Though some instituted large deductible programmes and various forms consider paying out of hastened self-insurance plans, others sought long-term solutions through captives, group captives and risk retention groups. In return for 60 cents per dollar as a their strategic investment of capital and human resources, these long- term care organisations gained more than just stability and consistency of success—well, it’s not. coverage—they gained an education in the world of loss prevention and The fact is, these high risk management to help provide continuity and buttress the volatility of the insurance cycle. payout ratios equate to Undoubtedly, those who developed their sense of capital preservation billions of dollars that using proprietary data and risk management techniques discovered the could be returned to the power of data analytics and predictive modelling, or risk analytics. By tapping into the full potential of available data, much of which is collected facilities and invested in in the routine business of providing care, these long-term care liability organisations revolutionised their approach to risk management, and are infrastructure, used in enjoying tremendous success. operations, or spent to At the other end of the spectrum, however, some insuring organisations improve risk management have resisted change, have clung to traditional risk management practices and have comparatively fallen behind. For these organisations, programmes.” opportunity knocks. It’s time to recognise the power and predictive ability of information, and apply the lessons learned by their pioneering counterparts that have already forged ahead. with additional sources of data for thousands of facilities, such as care Defining risk analytics processes, deficiencies, fire safety, staffing to acuity ratios and survey results, the potential for risk prediction is obvious. After the data is While every organisation strives to manage risks more effectively, many cleaned, transformed, indexed, benchmarked and passed through highly rely heavily on infrequent (i.e. annual or bi-annual) on-site assessments sophisticated predictive models, the potential is fully realised in its ability to evaluate risk. Not only is this a costly mechanism, it’s far less effective. to answer important questions such as, “Which facility is most likely to Consider a captive that insures two hundred nursing home locations experience a big claim?” and evaluates risk through site visits carried out by a team of nurses. Regardless of their skill, each nurse invokes some element of subjectivity, Risk management opportunities rendering the outcomes inconsistent. Furthermore, the mere presence of Incorporating risk analytics into any risk management programme an on-site visit modifies facility behaviour, making any findings dubious. allows for more confident, timely and accurate analysis of risks—and Moreover, on-site visits are subject to time constraints, which drastically fewer surprises. In a broad context, these tools equip an organisation with limit the quantity of information reviewed. the ability to connect the dots between underwriting, risk management and loss evaluation. Every phase of the continuum relies on the same Data analytics, on the other hand, is a quantitative methodology validated data source. As a result, a deeper understanding of the of examining data for the purpose of drawing conclusions about the relationship between quality and risk emerges. In more specific terms, information. When coupled with predictive modelling, it has the potential opportunities include the following: to answer very important questions about risk. The analytic process essentially breaks down the complex system of providing care into • Immediate adjustment for risk: Long-term care is a highly dynamic risk individual predictors. These predictors help drill down and recognise that environment, as is most healthcare risk. Each facility’s level of risk can not all quality problems are created equal: some apparent weaknesses progress rapidly with changes to resident acuity, staffing ratios, agency are mitigated by compensating factors, while other weaknesses, in dependency, and other factors. For example, in today’s long-term combination with other factors, exacerbate risk. Larger and more specific care environment, providers are admitting more short-term, higher- pools of data offer higher predictive precision. Fortunately, in the long- acuity residents. Without a corresponding adjustment to staffing ratios, term care industry, there is no shortage of very specific data. exposure results—exposure that must be addressed before it’s too late For the past 20 years, virtually all long-term care facilities have faithfully • Focus risk management dollars: The provision of healthcare services submitted billions of data elements and various quality measures to goes hand in hand with the issue of allocating limited resources, the Centers for Medicare & Medicaid Services (CMS). When combined and long-term care is no exception. Through predictive analytics, an US Captive . April 2009
  • 3. organisation can target risk management resources more effectively, gain the largest impact and achieve better results • Put claims in context: When claims occur, it’s important to place each “Incorporating risk claim in a proper context. Assume two facilities face a claim for a resident’s fall. The context of the claims may be quite different. One facility’s data analytics into any risk may demonstrate that the fall was an anomaly that occurred despite having excellent protocols and risk management practices, while the management programme other facility’s data may reveal a true risk management weakness. allows for more confident, Traditional insurance would treat these facilities equally; however, risk analytics provides the requisite facts to guide decisions about future timely and accurate risks and the wisdom to determine which claims to defend and which analysis of risks—and to settle • Cut through the politics: A data-driven emphasis on risk eliminates bias fewer surprises.” and reduces conflict. When the ‘data’ (versus a ‘person’) reveals a risk management weakness, it is less offensive and less emotional • Allocate premiums more fairly: A risk management programme founded on evidence-based data has the ability to more precisely allocate premium (offering skilled nursing, assisted living, and a continuous care retirement dollars according to risk. This opportunity is especially important for community (CCRC)). captives that assume risk for a broad spectrum of providers Successful pricing of policies is considered intellectual property and, • Reinsurance negations: Traditional underwriting providers, such when used strategically, will generate larger-than-average underwriting as Lloyd’s of London, have taken notice that some long-term care profits for these insurance organisations. providers have made great strides in risk analytics, which is increasing Overall, programme success is driven by more consistent and cost- underwriting comfort in the reinsurance/excess coverage arena. But effective underwriting, applying risk management dollars to the area of negotiating for the best premiums will require data that demonstrate a largest exposure (typically two-thirds of losses are attributed to 20 percent sound risk management programme of the facilities in a captive) and proactively managing loss ratios in order • Defence strategies: In the event that a claim requires a defence, an to achieve results that are better than half the industry norm. Additionally, organisation with essential risk management data is prepared to these advanced underwriting tools can lead to increased market share at defend. Too often, claims are being adjudicated long after the personnel the expense of the competition, by avoiding the high-risk insureds that involved are gone, or where charts are incomplete and paperwork is have simply been lucky, while rewarding the unlucky with a carefully missing. With a risk management programme based on data analytics, priced competitive offering. the information needed is quickly accessible. The success of these tools in the long-term care arena should leave many When conducted properly, risk analytics equips any risk-bearing entity in the industry wondering what other areas of healthcare professional with the tools needed to underwrite effectively, price accordingly, monitor liability could gain from a similar data-based approach, such as physicians risk in a timely manner, and target risk management dollars wisely. These and hospital groups. Comparable benefits could be realised in these tools are proven to deliver, compared to standard industry techniques market segments by finding similar risk data points and developing a and practices. parallel profiling system to determine corresponding risk predictors. Show me the money Tools that deliver Some captives may consider paying out 60 cents per dollar as a success—well, it’s not. The fact is, these high payout ratios equate to Primary users of this data-driven approach, which integrates risk billions of dollars that could be returned to the facilities and invested in analysis, risk management and loss control with predictive models, infrastructure, used in operations, or spent to improve risk management include operators with large self-insured retentions (SIR) and various programmes. True success cannot be claimed until loss ratios are insurance groups, including Lloyd’s of London syndicates and managed down to their lowest possible level. And that is what data OneBeacon; underwriting organisations, such as CFC Underwriting analytics, predictive modelling and risk analytics can offer organisations (Lloyd’s programmes); and wholesale distributors, such as AmWins, willing to harness the power of knowledge and technology—an opportunity Highland Risk, and London American. to manage loss ratios to their lowest possible level. Collectively, these organisations rely upon predictive modelling and risk analytics for establishing premiums, reinsurance, acquisition and sales costs, loss forecasts, proactive risk management, and other components Mary Chmielowiec is the executive vice president of insurance and Paul involved in building the terms and conditions issued to benefit an Marshall is director of insurance business development at PointRight Inc. insured. Most often, the insured is a US-based, long-term care provider Paul Marshall can be contacted at: paul.marshall@pointright.com US Captive . April 2009
  • 4. Lexington Office Park Phone: 781.457.5900 420 Bedford Street, Suite 210 Fax: 781.674.2254 Lexington, MA 02420 www.pointright.com

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