The Many Applications of Healthcare Analytics: Market Planning, Social Good, and Donor Acquisition

Transcription

1 The Many Applications of Healthcare Analytics: Market Planning, Social Good, and Donor Acquisition Speakers: Bill Stinneford and Chris Pedigo The opinions expressed are those of the presenter and do not necessarily state or reflect the views of SHSMD or the AHA Society for Healthcare Strategy & Market Development

2 Learning Objectives 1. Understand the broad nature of analytics and their use across multiple areas of healthcare 2. Understand analytics use in achieving a healthcare organization s mission and social goals 3. Identify areas within your organization that analytics may benefit your business growth objectives

3 Presented by Bill Stinneford COMMON ASSUMPTIONS ON BIG DATA IN HEALTHCARE

4 Common Big Data Assumptions Big data and predictive modeling is only used in clinical settings, not the business side of healthcare Data is only a good indicator of the past and not a predictor the future More data is always better

5 Debunking the Myths Big data and predictive modeling can be used in all areas of healthcare Data can be used to predict future performance Quality data is better than quantity

6 Laying the Foundation EFFICIENTLY USING ANALYTICS

7 How It Works: Not Big Data, Big Answers YOUR DATA Patient Data by Service Line, Visits, Facility Characteristics 3 rd PARTY DATA Census, Credit Card, Payor Type, Diagnostic Data, Brand Preferences, etc. TECHNOLOGIES GIS Software, Data Analytics Software, Data Visualization Partners, etc.

8 Patient Analytics Engine DEMOGRAPHICS PSYCHOGRAPHICS Tom King 1308 Center St Age: Income: Eats Organic $75 - Diet $100K Married Sees Family Physician Enjoys Running Takes Rx for Cholesterol Drinks Red Wine WHO DEFINE WHO YOUR BEST POTENTIAL & EXISTING PATIENTS ARE WHERE IDENTIFY WHERE YOUR BEST POTENTIAL & EXISTING PATIENTS ARE FOUND VALUE KNOW THE VALUE OF YOUR BEST POTENTIAL & EXISTING PATIENTS

9 Presented by Chris Pedigo COOK CHILDREN S CASE STUDY

10 About Cook Children s Six county service area in North Texas Referral area encompasses half the state 60 primary and specialty care offices Began using analytics in 2013 to better understand the location needs of broad primary care patient base

11 MARKET PLANNING

12 A01 A02 A03 A04 A05 A06 B07 B08 B09 B10 C11 C12 C13 C14 D15 D16 D17 D18 E19 E20 E21 F22 F23 G24 G25 H26 H27 H28 H29 I30 I31 I32 I33 J34 J35 J36 K37 K38 K39 K40 L41 L42 L43 M44 M45 N46 N47 N48 N49 O50 O51 O52 O53 O54 O55 P56 P57 P58 P59 P60 P61 Q62 Q63 Q64 Q65 R66 R67 S68 S69 S70 S71 Percent 12 Core Primary Care Patients 12.1 Primary Care 10 Primary Core patients are the most likely to be your patients. Patients Trade Area 6% 6% 88% Selfpay Commercial Government 8 6 Secondary Core patients are not as likely to be your patients as Primary Core patients but are still very likely. Tertiary Core patients are less likely to be your patients but are opportunity segments Primary Secondary Tertiary Non-Core Segment

13 Core Neighborhood Clinic Patients A01 A02 A03 A04 A05 A06 B07 B08 B09 B10 C11 C12 C13 C14 D15 D16 D17 D18 E19 E20 E21 F22 F23 G24 G25 H26 H27 H28 H29 I30 I31 I32 I33 J34 J35 J36 K37 K38 K39 K40 L41 L42 L43 M44 M45 N46 N47 N48 N49 O50 O51 O52 O53 O54 O55 P56 P57 P58 P59 P60 P61 Q62 Q63 Q64 Q65 R66 R67 S68 S69 S70 S71 Percent Segment Patients Trade Area 5% 7% 88% Neighborhood Care Selfpay Commercial Government Primary Secondary Tertiary Non-Core

14 Applying the Insights Priority is expanding primary care clinics through both de novo and acquisition Use the model to understand where there are opportunities

15 SOCIAL GOOD

16 Preventing Child Maltreatement Tarrant County has highest rate of child maltreatment in the state Used analytics to identify areas of focus for outreach and prevention

17 Analytical Approach Use analytics to identify specific risk factors Identify specific neighborhoods and block groups where children are most at risk Focus efforts in at-risk block groups and monitor/track progress.

18 Current State of Program Continue working to define necessary capacity for prevention services by abuse type (physical, sexual, neglect). Identify contributory risk factors to guide focus and alignment of services. Associate co-located health, crime, and social problems to identify opportunities for collaboration. Utilize market segmentation for creation of centralized, coordinated, targeted campaigns to address problematic social norms where they occur. Develop recruitment campaign via insight from segmentation analysis for foster and adoptive family onboarding in collaboration with child placement agencies and the Department of Family and Protective Services (DFPS).

19 DONOR ANALYTICS

20 Goals of Analysis Identify potential new donors to support the organization s mission Enhance donation contributions from existing donors

21 A01 A02 A03 A04 A05 A06 B07 B08 B09 B10 C11 C12 C13 C14 D15 D16 D17 D18 E19 E20 E21 F22 F23 G24 G25 H26 H27 H28 H29 I30 I31 I32 I33 J34 J35 J36 K37 K38 K39 K40 L41 L42 L43 M44 M45 N46 N47 N48 N49 O50 O51 O52 O53 O54 O55 P56 P57 P58 P59 P60 P61 Q62 Q63 Q64 Q65 R66 R67 S68 S69 S70 S71 Core Donor Households Profile To be a core donor, a segment must comprise at least 1% of the donor base, and are then differentiated as follows: Primary Core Donors are the most likely to contribute to Cook Children s Health Foundation. Secondary Core Donors are not as likely to contribute as Primary Core Donors, but are still very likely. Tertiary Core Donors are less likely to contribute to Cook Children s Health Foundation, but are opportunity segments. 19% 12% 10% 8% 6% 4% 2% 0% Segment

22 Selection of Donors for Profiles The four metrics were combined to give each household a group score. Recency grouped by latest donation date Score 1: August 2010 and earlier (more than 5 years ago) Score 2: September 2010 to February 2013 (between 2.5 years and 5 years) Score 3: March 2013 to August 2014 (between 1 year and 2.5 years) Score 4: September 2014 to August 2015 (within 1 year) Frequency grouped by units Score 1: 1 donation Score 2: 2-5 donations Score 3: 6-12 donations Score 4: 13 or more donations Monetary Value grouped by total donation amount Score 1: Up to $700 total in donations Score 2: $700 - $2,999 total in donations Score 3: $3,000 - $9,999 total in donations Score 4: $10,000 total or higher in donations Proximity grouped by miles from Cook Children s main campus Score 1: 50 or more miles Score 2: miles Score 3: miles Score 4: Under 10 miles

23 Selection of Donors for Profiles Only non-employee donor households donating under $100,000 total were considered in the RFMP analysis. From this selection, 4,410 donor households who donated more than one time and within the last five years were selected as the Core Donors for this profile analysis. Monetary Value for Core Donor Group Monetary Value Core Donors Total Donations Average Donation $700 or less 3,148 $619,245 $197 $701-$2, $1,072,965 $1,372 $3,000-$9, $1,450,229 $5,312 $10,000 or more 207 $5,890,458 $28,456 Grand Total 4,410 $9,032,897 $2,048 5

24 Three Key Take-Aways 1. The basic principles of analytics can be used to make decisions across all areas of healthcare organizations 2. Focus on who, where, and value to get the most out of your analytics 3. Analytics can be used to support your mission and social goals

25 Questions? Bill Stinneford Chris Pedigo The opinions expressed are those of the presenter and do not necessarily state or reflect the views of SHSMD or the AHA Society for Healthcare Strategy & Market Development