How To Understand The Different Sides Of Cost Containment In Healthcare Insurance

Transcription

1 The Different sides of Cost Containment in Healthcare Insurance : translating to Quality of Care, Policy Management, Medical Contracting and Fraud, Waste and Abuse Ross Kaplan Solutions Architect SAS Security Intelligence - Healthcare

2 Agenda o o o o o o o Challenges in Healthcare The Scope (expense) of the Problem The Analytics (Big Data) Challenge Cost Containment key issues Complexities of Healthcare Fraud Options for Waste and Abuse How to Improve current containment efforts 2

3 Worldwide Healthcare Challenges o Inconsistency/diversity of medical standards, regulatory oversight and enforcement actions across regions and countries o Wide variances in procedure costs across regions and countries, with lack of centralized comparative cost data o Lack of boots on ground investigative resources in many regions or countries o Inevitable advent of fraud, waste and abuse related to growing medical tourism trend o Emergence of international marketing of health care services e.g., adverts in U.S. in-flight magazines for South America cosmetic surgery clinics; Singapore surgical centers; European Medical Tourism

4 Some New Challenges in Healthcare o Trends like the growing adoption of Electronic Medical Records are driving an explosion in available data o The number of people using the healthcare system is increasing o Expectations for improving quality of care, cost of care, and patient satisfaction are increasing o Genomic medicine on the horizon o Limited resources to build reports and the subsequent time to define requirements 4

5 Why Should You Care? o Preventable medical errors are the 6 th leading cause of death in the United States, with an estimated 98,000 deaths annually. That s more than diabetes at 75,000 deaths annually. 2005, Centers for Disease Control (CDC) o 1 out of 5 Medicare patients involved in a patient-safety incident die as a result. 2008, HealthGrades o Medication errors harm 1.5 million people in the United States each year costing billions. 2006, Institute of Medicine 5

6 Why is this Relevant? Big Data analytics has the potential to change the game in healthcare Examples include: Machine-learning-based diagnosis support systems at the point of care can help prevent medical errors and improve quality Analysis of electronic medical records can be used to detect missed diagnoses, monitor medication compliance, and much more Aberrant behavior detection Provider profiling 6

7 Perspective on Cost of Health Care Fraud EHFCN Newsletter, March April 2010 Estimated global dollars associated with health care fraud ( 160 / 180 / $260 billion each year) is enough to: Provide clean, safe water around the globe Bring malaria under control in Africa Provide the Diphtheria, Tetanus and Pertussis vaccine to all 23.5 million children under one years old who are currently not immunized (2.5 million die each year from diseases preventable by vaccines) AND quadruple the budget of the World Health Organisation and UNICEF (the United Nations Children s Fund) with more than 100 billion left over enough to build more than 1,000 new hospitals at developed world prices

8 Common Healthcare Data sources Claims Pharmacy Claims Member/Beneficiary Provider/Physician Call Center Facility Nurse Notes Provider/Physician Notes External sources Most analysis of adherent behavior is associated to claims information Can be independent of regular claims and stored in a separate system, however in most case are received as regular claims. General member data: age, region, gender, etc.. However, it can be severally limited. For example no information on medical information (i.e. weight, diet, medical issues, etc.) General provider/physician data: specialty, license number, address, name, etc. This is generally more useful in member/beneficiary fraud, and requires text mining to put free-form text in a structured format This could apply to any physical facility. Such as a hospital, clinic, provider s office, or DME supplier Notes written by either a nurse on-site with the patient or over the phone. This information also and requires text mining to put free-form text in a structured format. Many institutions (fed, local & private) do not have this type of data available. Same as above, except for providers. Data can come from external sources (other agencies, customers, private carriers) or data can be purchased.

9 Key Elements of Healthcare Data Analytics o Efficiency Extensive analytical experience leads to shorter time to completion o Big Data Read, clean, and analyze data with tens/hundreds of millions of rows (and larger) o Analytics Building models to assist in peer grouping and predicting providers worthy of investigation o Social Network Analysis Explore connections between providers for potential fraud rings o User Interface Customize interface to provide investigators with quick access to necessary information o Oversight of Process Assign cases (managers) and disposition claims (investigators)

10 Organization s direction for Cost Containment Focus of the organization Health care fraud and abuse only? Policy modification Specialty: Workers Comp, Disability fraud? Provider Abuse? Contract negotiation Quality of Care (consistent level of service) Patient Abuse? Medical Devices? Hospitals Contract negotiation

11 Issues facing Cost Containment for Healthcare o Pre-Payment o Data Issues Many current infrastructures do not support for it. Claims systems do not allow for direct access so no real-time analysis (at best, nightly extracts to a data-warehouse) Payment rules differ by region regarding when claims need to be paid limiting time allowed for investigation Investigative unites are not of sufficient size to handle the turn-around required by the time required to pay claims Issue with alienating providers if legitimate claims are not paid Most private healthcare insurance carriers, state/local and government have admitted that they have missing or bad data. Internal organizations do not share data well, and have no automated method of communication or analysis

12 Quantifying differences o Quality-of-care Many private organizations are concerned with detecting other types of negative behavior that can be used for both detecting abusive behavior (for policy management) as well as providing better, more consistent member care o Variances by Region The regions (countries) through-out EMEA are similar, but each has it own nuances and healthcare privacy laws Health insurance is not international (limited to a specific regions + ex Pats)

13 Regional Limitations o Poor information Sharing within regions It appears that most countries/regions, do not share any FWA information that includes detection methods, processes, best practices, etc. And appear to have limited (or no) capability to access any resources that are experts in healthcare fraud (for international best practices o Limited capability of investigators to capitalize on analytics (fraud, waste abuse and quality of care) Since many regions do not use advanced analytics to identify aberrant behavior they appear to have not developed the investigative methods associated to purposing non-linear behavior. This makes it difficult for these organizations to see value, either long-term or shortterm from the analytics or improved automation

14 Regional Limitations (cont.) o Best Practices are not applied Due to the limited information sharing, many regions are unable to exercise best practices and are forced to re-invent the wheel for most cost containment practices. There also appear to be limited consulting associated to cost containment, and practices are only focused on a single aspect of the problem.

15 Scope of the Problem : Europe just for Fraud Country In Bn Germany 13,016 France 10,576 UK 8,554 Italy 7,021 Spain 4,328 Netherlands 2,687 Belgium 1,664 Sweden 1,527 Austria 1,394 Estonia 1,261 Greece 1,078 Poland 900 Portugal 839 Finland 722 Ireland 709 Hungary 398 Romania 235 Slovakia 168 Bulgaria 97 Lithuania 79 Latvia 57 Cyprus 48 Czech Republic NA o Equates to approximately 5.5% of healthcare spend across Europe a very conservative estimate o Waste and Abuse could be up to 20% to 30% of total cost of claims

16 Typical Steps in the Investigation Varies by Case (not an easy solution) o Request documentation Medical records Invoices Second surgical report Operative notes o Interviews (patients or provider) o Surveillance and outside consultant reviews o Referral to law enforcement o Final letter showing reason code for outcome or request for recovery never use the word fraud unless prosecution has occurred. o Prepare for prosecution (if applicable)

17 Common Types of Provider Fraud, Waste, Abuse Overutilization Upcoding False Claims Unbundling Billing for Non-Covered Treatments Fraudulent Dates of Service Waiver of Co-pay Free Medical Service Kickbacks Phantom Providers Misrepresenting Medical Records Billing for services not medically warranted, to receive insurance payments, or falsifying diagnosis to justify medically unnecessary procedures. Using a code for a more expensive treatment than what was performed. Billing for services not performed or supplies not provided. Improper submission of separate claims for services that should be combined under a global fee. Billing for non-covered treatment as though they were covered treatment (e.g. experimental not covered by insurance plan). Falsifying the date to avoid contract limitations on eligibility or payment maximums. Waiving coinsurance or deductible to accept insurance as payment in full, and then inflating charges to insurer. Free service to patient, then billed to insurer, to entice ongoing other treatments. Providers receiving cash payments in exchange for driving business to certain ancillary providers (e.g. labs). Unlicensed providers posing as physicians. Falsifying the medical records to justify services that were not provided or not warranted.

18 Common Types of Member Health Claim Fraud, Waste and Abuse False Claims Collusion Speculation Application Fraud Identity Theft Disability Fraud Patient misrepresents services and submits false claims for reimbursement. Patient and provider collude to submit false claims, typically with provider returning portion of reimbursement to patient for cooperation. Patient has multiple individual health insurance policies without revealing other coverage and collects on all. Misrepresentation of material statements on application for insurance in order to obtain coverage that would be denied or modified. Member has SSN or Benefits ID stolen for purposes of someone else receiving insurance benefits, or sells or rents their ID to another for an access fee. Patient misrepresents the nature or extent of a disability or misrepresents loss of income to obtain higher benefits.

19 Investigations Unit Authority o o Varies by company/organization Health care fraud and abuse only? Dental, Workers Comp, Disability fraud? Patient Fraud? Broker or agent fraud? o Subsidiaries and all business units, or divided by LOB? Investigation units often owns the Fraud/Waste & Abuse problem but may not own the budget for solutions or prosecutions o Reporting hierarchy Legal/Audit/Compliance Operations (less frequent) Finance/CFO Medical Economics/Informatics/Managed Care

20 Controlling the cost of Waste and Abuse o Policy Management Abuse can be easier addressed through the modification of what (or how much ) services providers will be reimbursed for by policy adjustment o Contraction Negotiation Many organizations directly negotiate with hospitals for the compensation for services provided and have little analysis on consistency of quantity and cost of those services

21 Advantages of improved Analytics END-TO-END CAPABILITIES

22 Methods for Improving Analysis» Scenario weights what violations are most important to Insurance Company» Scenario thresholds how bad should the violation be to flag?» Custom clinical rules based on Insurance Company s set of intervention codes» Custom scenarios what additional issues are not getting caught by the current adjudication system that you would like to see flagged» Fine tuning of peer groups based on external information not found in the claims» Scaling aggregate data based on external information about the provider

23 Areas of Focus for Cost Containment o o o Fraud: Early detection prevent more losses Improved investigation data better for case creation (and court) Support for pre-payment Abuse: Early identification Clear classification of abusive behavior = better prevention More directed education of aberrant providers (or other entities) Identification of group/collective abuse (other entities involved) Patterns of abuse associated behavior Dynamic profiling avoiding new abuse Waste: Clearly identify areas of process improvement Clearly identify areas for policy modification Assist in contract creation (particularly in middle and large group)

24 Methods to reduce costs in Health Care o More prioritized Fraud, Waste, & Abuse cases identified Including both previously undetected entities and networks and extensions to already identified cases o Reduction in false positive rates Improved analysis and information sharing can greatly reduce false leads o Improved analyst / investigation efficiency Improve time to investigate due to data aggregation and visualization Provides alert logic and suggested path to initiate investigation o Data Quality: Eliminates noise - leads to better analytics leads to more effective investigations o Improved contract negotiations with Providers and Hospitals o Better information for Policy modification