7. Analytic & Vendor Mgmt - Health Flashcards
Define “data analytics” in the healthcare context
The process of inspecting, cleaning, transforming, interpreting, and modeling data to discover trends, patterns, and other information that can support benefit plan decisions & changes.
Goals:
1. Reduce costs
2. Improve clinical outcomes &/or the participant experience
Define “predictive modeling” in the healthcare context
A statistical technique commonly used to forecast future behavior. It involves analyzing historical and current data to generate a model to forecast future outcomes. Can be used to quantify risk & costs for individuals & groups of folks enrolled in health plan.
List 6 ways in which predictive modeling can be leveraged by health plans
- Review a plan’s disease burden (health status) & how it will change over time
- Stratify plan’s population by risk level to identify at-risk & catastrophic claimants for targeting disease mgmt & case mgmt, respectively
- Identify risk factors likely to generate future plan costs that should be targeted w more intensive outreach, including finding at-risk individuals who, although may be low cost today, may generate significant costs in the future
- Compare relative resource consumption* by groups for budgeting & underwriting forecasts
*refers to how intensively plans use physician visits, hospital stays, & other member resources - Compare providers fairly, adjusting for differences in health risk among patient pop’ns. Such comparisons can be used to profile providers for utiliz review & quality of care
- Analyze a medical mgmt program to see what the true savings are, as opposed to those that are regression to the mean *outcomes that are at least partly due to chance; refers to the phenomenon of “averaging out” in statistics
Explain (8 pts) how health plan sponsors use data analytics & predictive modeling
- Identify claims trends
- Target high-risk users
- Identify gaps in care
- Steer patients to best providers
- Measure vendor perf
- Uncover cost-sharing strategies
- Engage participants in their own care
- Investigate waste, abuse, fraud
List the 5 recommended steps plan sponsors should take to implement data analytics & predictive modeling tools
- Determine who will perform the data analytics
- Use data analytics & predictive modeling to identify & map the most prevalent clinical risk characteristics & associated costs in the plan pop’n
- Establish a 3y health-mgmt strategy
- Develop a formal participant comms strategy
- Identify how plan participants will react to change
There are 5 recommended steps plan sponsors should take to implement data analytics & predictive modeling tools. Describe the step “1. Determine who will perform the data analytics.”
Only the very largest plans have the capabilities to handle data analytics on their own. Most need to decide whether the analytics offered by their existing vendors are sufficient, or if they should outsource.
There are 5 recommended steps plan sponsors should take to implement data analytics & predictive modeling tools. Describe the step “2. Use analytics/modeling to identify & map prevalent clinical risk & costs”
Plan sponsors should evaluate the programs in place to address clinical risk characteristics and associated costs in the plan population.
There are 5 recommended steps plan sponsors should take to implement data analytics & predictive modeling tools. Describe the step “3. Establish a 3y health-mgmt strategy”
Strategy should have a budget, goals, and performance targets that increase over time.
E.g., improve wellness participation from 10% year 1, to 50% year 2, 75% year 3
There are 5 recommended steps plan sponsors should take to implement data analytics & predictive modeling tools. Describe the step “4. Develop a formal participant comms strategy”
While data analytics can reveal the cost outliers to plan sponsors, effective comms can have an immediate, direct, positive impact
There are 5 recommended steps plan sponsors should take to implement data analytics & predictive modeling tools. Describe the step “5. Identify how plan participants will react to change”
It’s important to remember that any changes a plan sponsor implements affects people directly.
Identify 3 macro trends that presently impact the day-to-day operations of most HR teams, with details
- Rising healthcare costs: Healthcare consistently outpaces inflation and makes up one of the largest line items in almost every company budget
- Budgets are under stress: HR depts are being asked to do more with less. COVID accelerated the trend in many industries, and even in unaffected industries, uncertainty surrounding future variants has led companies to be more risk-averse & restrict spending
- Workforce shifts: Attracting & retaining best talent is key to company growth & sustainability. Economic changes post-COVID have given EEs more choice than ever, leading to the largest talent acq and retention upheaval in 20+yrs
Identify 4 types of analytics & the core questions they answer
- Descriptive: what happened
- Diagnostic: why it happened
- Predictive: what might happen
- Prescriptive: recommended actions
Cute examples where the use of descriptive & diagnostic analytics can provide insights to plan sponsors re: emerging hc trends impacting their plans
Many situations arose re: pandemic.
COVID brought about many changes in the types of care folks sought, incl. rise in mental health utilization. By understanding change in EE needs like these, companies modified existing plans & searched for vendors to help them offer bens that served these expanding needs.
Deferred care also arose re: COVID. Many skipped/delayed care during pandemic. When health problems arise from lack from treatment, ER often see more expensive remedial treatments. To address deferred care, companies can take preemptive action to engage partners to boost annual screenings/physicals.
Pandemic saw rise in telehealth adoption. ERs altered plan design & comms to encourage uptake and drive savings for ER & EE.
In all 3 cases, descriptive & diagnostic analytics helped companies get a pic of what happened w their plans to address emerging trends.
What 8 health plan trends have recently emerged that were documentable by descriptive & diagnostic analytics?
- Shift away from single health plan offerings
- More moderate health plan premium increase than originally projected at pandemic onset
- ER absorbing a larger % of health premiums for their EEs
- EEs participating in vol benefits, regardless of health plan choice
- Increased participation in HDHPs
- High HDHP selection level by Millennials
- Increased enrollment & contributions to HSAs
- Average total HSA contribs exceeding 60% of IRS statutory limits
How can individual EEs utilize predictive analytics to take advantage of their ERsponsored health plans?
Decision support tools leverage health claims data from the previous year, allowing EE to predict OOP costs for each plan choice available to them.
EE can customize their expected usage based on what they expect to happen, e.g. birth of a child, to project & minimize OOP costs.
Identify situations where ER use of predictive analytics could yield favorable financial outcomes for the ER
Measure the financial impact of plan changes across workforce, when considering plan provision changes. E.g., increasing ER and inpatient copay, increasing single deductible, adding family deductible.
Can identify what % of population would be affected by a change, & how individuals would be impacted.
Can estimate the ROI of a vendor program.
How can ERs continuously learn from & improve their benefits programs by combining various analytic tools?
By combining data, computerized analysis algorithms, and automated actions.
E.g., in managing a Rx drug plan, plan sponsor could conduct ongoing analysis of Rx claims data, proactively identifying maintenance Rx with cheaper alternative, and automate process of getting the Rx switched.
Greater transparency would arise from putting drug cost info in the prescriber’s hands and take effort out of finding beneficial alternatives. Rx behavior change could create savings that complement the ER’s PBM/pharma consulting strategy, leading to a reduction in Rx spending.
Higher satisfaction can also occur for plan members bc they receive needed meds more affordably, keeps them healthy, improves medication adherence, saves money without effort on their part.
What 6 criteria should a hc plan sponsor discuss with / ask a potential data analytics vendor?
- Completeness of vision: Plan sponsors should look for vendors that can clearly outline how they have evolved to meet & anticipate industry needs.
- Culture & values of senior leadership: The overall culture of a company starts at the top. Plan sponsors should get to know the senior leaders of the vendors they evaluate, and insist on meeting several members of the exec team. Is there alignment between vendor & sponsor leadership?
- Ability to execute: Do the vendors in consideration have solid, referenced accounts, similar in size & demographics to plan sponsor’s own?
- Tech adaptability & supportability: Underlying engineering & architecture of software. Plan sponsors, peel back the covers of vendor’s products; evaluate software engineering for modern design patterns
- Total cost of ownership: Plan sponsors must understand total cost of vendor solution
- Company viability: Will the vendor be around in 9y, the average lifespan of a significant IT investment?
List the 10 key considerations for success when a hc org is implementing an analytics initiative:
- Data modeling & analytic logic
- Master reference/master data mgmt
- Metadata repository
- Managing white space data
- Visualization layer
- Security
- Extract, transform, load (ETL)
- Performance & utiliz metrics
- Hardware, software infrastructure
- Cultural change mgmt
Explain this key consideration for success when a hc org is implementing an analytics initiative: “Data modeling & analytic logic”
Different vendors’ analytics solutions feature different data models. Which data model they use can have a significant effect on cost, scalability, & esp the adaptability of plan sponsor’s analytics solution to support new use cases
Explain this key consideration for success when a hc org is implementing an analytics initiative: “Master reference/master data mgmt”
The ability to incorporate data from new and disparate sources into the plan sponsor’s analytics solution requires significant expertise in master data mgmt.
Explain this key consideration for success when a hc org is implementing an analytics initiative: “Metadata repository”
Plan sponsors should look for a vendor that provides a tightly integrated, affordable, simple repository with its overall analytics solution
Explain this key consideration for success when a hc org is implementing an analytics initiative: “Managing white space data”
Does the plan sponsor’s analytics solution offer a data collection alternative to the proliferation of desktop spreadsheets and databases that contain analytically important data? white space data is the data collected & stored in desktop spreadsheets and databases that is not being collected and managed in primary source systems, especially electronic medical records (EMRs), or it is being collected in clinical notes and must be manually abstracted for reporting & analysis. This desktop data fills in the missing “white space” of analytic info that is important to the org.
Explain this key consideration for success when a hc org is implementing an analytics initiative: “Visualization layer”
The best analytics solutions include a bundled visualization tool - one that is both affordable and extensible if licensed for the entire org. However, the visualization layer is very volatile The leading solution today won’t be the leader tomorrow. Therefore, plan sponsors should look for an analytics vendor that can quickly and easily decouple the underlying data model and data content in the data warehouse from the visualization layer and swap the viz tool with a better alt when necessary.