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BIG DATA ANALYTICSIN DENTAL PUBLIC HEALTH RESEARCH:THE PROMISE OF INTEGRATION
DentaQuest Partnership Continuing Education Webinar September 17, 2020
DOI: 10.35565/DQP.2020.3016
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Learning Objectives
By the end of this webinar, participants will be able to:
1. To improve knowledge of big data analytics and its limitations within the current system
2. To better understand how the application of data, through analysis of diabetes and antibiotic stewardship, can improve and maintain the health of patients
3. To highlight and identify where policy, care, and interoperability gaps exist, and how closure could significantly improve outcomes
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Housekeeping
• All lines will remain muted to avoid background noise.• A copy of the slides and a link to the recording will be shared after the webinar
concludes. • In order to receive CE credit you must fill out the webinar evaluation, which
will be shared at the end of the presentation. The evaluation must be completed by EOD Friday, September 25 to receive CE credit. CE certificates will be distributed a few days after the webinar takes place.
The DentaQuest Partnership is an ADA CERP Recognized Provider. This presentation has been planned and implemented in accordance with the standards of the ADA CERP.
*Full disclosures available upon request
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Q&A Logistics
After the presentations we hope to have some time for Q&AWe will be monitoring the chat box through the entire presentation and we will do our best to answer all questions.• Type your question in the chat box
and make sure you send it to allpanelists.
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Presenters
ANALYZING “BIG” AND ELECTRONIC HEALTH RECORD DATAEric TranbyManager, Data and [email protected]
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What is Big Data?
https://dzone.com/articles/why-is-big-data-in-buzz
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Common Uses of Big Data
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From Big Data to Better Patient Outcomes
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Using Big Data for Oral Health Research
• Sources: dental claims, integrated medical and dental claims, practice management software, dental imagery, clinical notes, genetic information.
• Key limitation: Lack of common diagnostic coding or categorization.
• Research with Big Data is NOT like research with survey, exam, or clinical data.
• Not collected with research in mind
• Ill-defined or not defined variables
• Stored in multiple tables, multiple units of analysis
• Requires significant storage and computing power
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Analytical Strategies with Big Data
1. “Traditional” statistics work, with modified assumptions: 1. Often are population parameters.
2. Measures of variance are either very large or very small.
3. Statistical significance means less, magnitude of effect is more important.
2. Better techniques are those optimized for use in large datasets1. Predictive techniques – Powered regressions, patient similarity, decision
trees, neural networks
2. Probabilistic modeling – Clustering methods, latent class, fuzzy set, association rules
3. Key: The machine learns, but does not interpret or understand.
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Practical Tools for Analyzing EHR and Big Data
1. Storing Data:
• Languages/Software: SQL, Python, JavaScript, Hadoop,
2. Analyzing Data:1. Non-Distributed Data - R, SAS, Stata
2. Distributed Data – Python, R, Hadoop, JavaScript,
Relational Database
Data Warehouse Data Lake
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Big Data Example – Oral Health Over the Lifespan
• Integrated Medical and Claims Data • IBM Watson Medicaid and Commercial in 2017
• Categorization of Dental Claims and Medical Claims for Dental Conditions• Dental Care
– Diagnostics, Imaging, Preventive, Minor Restoration, Major Restoration, Endodontics, Oral Surgery, Periodontics, Prosthodontics, Orthodontics, Anesthesia, Adjunctive General
• Medical Care for Dental Conditions– Dental Care by PCP, Ambulatory Surgery, ED visits, Inpatient Admissions, Oral
Cancer, Rx
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Big Data Example – Oral Health Over the Lifespan
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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88
Preventive and Basic Dental Procedures Major Dental Procedures Medical Care for Non-Traumatic Dental Condtions
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Advantages1. Deeper knowledge2. Faster, Actionable Insights3. Differentiation of Effects4. Optimization and Personalization
of Findings and Care
Disadvantages1. New skills and resources needed2. Investment in data preparation3. Generalizability can be difficult4. ML/AI can perpetuate inequalities
Why Big Data in Oral Health Research?
Challengesencountered during working on medical-dental Electronic Health Records (EHR) data.
Munder Ben-Omran BDS, MS
Post Doc Fellow, Dental Public Health Informatics, NIH/NIDCR
Nature of relative relational databases and overview about EHR-data source structure
Challenges encountered with examples from integrated EHR projects
Examples of data handling and wrangling
Conclusion and take-home message
Data Characteristicsand Structure
Nature of Relational Databases “RDBs”
Nature of RDBs and other live big data
Changing information requires updating all files related
• Hierarchical model• Network model • Relational model
Multiple files with same topic but
different information
Multiple Time points
Challenges
Different layers of
tables (complexity)
+Large tables
Missingness and variable distribution
Designed for administrative
purposes
Codebook limitations
Significant front-end
data management
work
More time needed for processing
codes scripts
Handling data
• Overcoming data issues:• Data reading
• Codes examples • Data manipulation
Live examplesfrom
HCN dataSafety Net
Organizations
52
States20
7,900,000Total Patient Visits
2.1 MillionUnique Patients
Research Questions• To identify the demographics characteristics and specific diagnoses of adults that have received antibiotics and other
medications.
• To identify the demographics and characteristics of adults that have prediabetes, diabetes patients that could be cross referred during
both dental and medical encounter.
• Requesting medical and dental data
• Aged > 20 from 1/1/2013 to 1/25/2016 to identify the number of antibiotics and other medications prescribed within 30 days of a given diagnosis.
• Level of Analysis to Done:• Demographic Level• Total Number of diagnoses Level
• Both dental and medical
• Aged > 20, period of 30 days before encounter and 1 year after. Referral codes
• Level of Analysis to Done:• Demographic Level• Total Number of diagnosis and referrals
Cohorts and Characteristics
Methodology• Specific descriptive analyses conducted include the following:
• Medical Condition/Diagnoses Level Total• Dental, Genitourinary, Respiratory, and Other Diagnoses
• Medication Status : Antibiotics only prescribed• Antibiotics and other medications prescribed• Other medications (not included above) prescribed• No medication prescribed
• Specific descriptive analyses conducted include the following:• Prediabetes, diabetes and periodontitis condition• Medical and Dental Encounter
• Referral codes• Diagnosis codes
Available Dataset for Analysis
• Import Data• Data imported from Intergy Data Feed
Front end data collection
Data storage and management
Data analysis
Requested Data ElementsDiagnosesTables Needed
MedicationTable Needed
Requested Data ElementsDemographic Tables Needed
Dx file: dental, uro, resp, other
Other Medication
No Medication
From the Procedure Feed/Table, ICD9/10 codes were extracted. We created 4 Diagnoses.
Dx + RX
Dx file Created: Dental, Resp, Genito, Other
Merge with Diagnoses file
Other Medication
This merge was done made choosing the RX date closest to the DX date
Create file that identifies patients and then match with appropriate diagnosis.
A Patient ID File
Dx file: Dental, Resp, Genito, Other
Patient ID File
Antibiotic
Dx file: dental, uro, resp, other
Merge with Medication Table
Other Medication
No Medication
This merge was done made choosing the RX date closest to the DX date
No merge necessary, no medication or most recent RX date >30 days of DX
Dx+Patient ID and Rx with categories:If for each DX:1. no medication prescribed,2. Only antibiotic, 3. other meds and antib.4. other meds (excludes meds mentioned in #3)
Dx+Patient ID + RX
A
B C
Patient ID File
Dx file: Dental, Resp, Genito, Other
A
Medication
D
Demo
E FDemographics extracted from Patient Feed and matched with appropriate patient id in merged file
Patient ID File
Dx file: Dental, Resp, Genito, Other
Analytical File Created: Can generate various reports
Demo
D E
Antibiotic use by race
Antibiotics prescribed by sex
Antibiotics prescribed by diagnosis
DiagnosisTable Needed
Medication ad lab Tables Needed
Requested Data Elements
Demographic Tables Needed
Requested Data Elements
final status Frequency PercentCumulativeFrequency
CumulativePercent
Normal 153418 61.63 153418 61.63No Controlled 84008 33.75 237426 95.37
Controlled 11519 4.63 248945 100.00
Have a dental visit a year before or six month after
status Frequency PercentCumulativeFrequency
CumulativePercent
0. No Dental Visit 241775 97.12 241775 97.12
1. With Dental Visit 7170 2.88 248945 100.00
Have a dental visit a year before or six month after
status detail Frequency PercentCumulativeFrequency
CumulativePercent
1. No dental Visit 241775 97.12 241775 97.12
2. Dental visit year before 4583 1.84 246358 98.96
3. Dental visit 6 months after 2587 1.04 248945 100.00
Sex Frequency PercentCumulativeFrequency
CumulativePercent
F 149152 63.63 149152 63.63M 85254 36.37 234406 100.00
Frequency Missing = 14539
race_eth Frequency PercentCumulativeFrequency
CumulativePercent
Hipanic 102511 43.80 102511 43.80NHB 53820 23.00 156331 66.80NHW 65939 28.18 222270 94.98Other 11750 5.02 234020 100.00
Frequency Missing = 14925
final status status Frequency PercentCumulativeFrequency
CumulativePercent
Normal 0. No Dental Visit 148913 59.82 148913 59.82
Normal 1. With Dental Visit 4505 1.81 153418 61.63
No Controlled 0. No Dental Visit 81545 32.76 234963 94.38
No Controlled 1. With Dental Visit 2463 0.99 237426 95.37
Controlled 0. No Dental Visit 11317 4.55 248743 99.92
Controlled 1. With Dental Visit 202 0.08 248945 100.00
final status Status detail Frequency Percent CumulativeFrequency
CumulativePercent
Normal 1. No dental Visit 148913 59.82 148913 59.82
Normal 2. Dental visit year before 2954 1.19 151867 61.00
Normal 3. Dental visit 6 months after 1551 0.62 153418 61.63
No Controlled 1. No dental Visit 81545 32.76 234963 94.38
No Controlled 2. Dental visit year before 1455 0.58 236418 94.97
No Controlled 3. Dental visit 6 months after 1008 0.40 237426 95.37
Controlled 1. No dental Visit 11317 4.55 248743 99.92
Controlled 2. Dental visit year before 174 0.07 248917 99.99
Controlled 3. Dental visit 6 months after 28 0.01 248945 100.00
Lessons learned
Organize and follow• Organize and
follow an analytical approach
Define how to mergeusing big
data tables
Plan ahead to save resources
and computing
time
Define timemerges, such
as 30 days between Dx date and Rx
date
Understand the nature of the
data: administrativedata present challenges to
produce desired analytical data
set for research
Keep a detailed
step-by-steplog
Take home message
Electronic medical and dental records analysis requires sophisticated
approach
Analytical process should considerunderlining data wrangling steps
Carefully plan the Techside
Using Big Data to Understand Dental Care in the Primary Care
Setting
Tamanna Tiwari MPH, MDS, BDSAssistant Professor
University of Colorado
Does medical and dental integration improve overall health? Gone are the days of treating dental disease in a vacuum – we now know that by increasing
our focus on prevention and early intervention, we can lower the cost of and
need for medical and dental treatment in the long run - AHIP
The discussion paper focuses on the weak links in the integration process of communication, coordination, and
referral across professions. NAM commentary, 2018
Some private insurers have begun to support this work, reasoning that because the mouth is the
gateway to the rest of the body, oral health impacts the cost of treating other medical
conditions and vice versa. The commonwealth fund, 2015
Medical-Dental Integration for Children
Research Question: What is the relationship between Well Child Visit (WCV) & Preventive Dental Visits (PDV)? Which locations have the most integrated care?
Methods
• Medicaid Data from 2016 and 2017 on Children, Ages 0-20.• WCV in 2016: ICD-10 codes Z00121, Z00129, Z00110-Z00111, Z005,
Z0070-Z0071, Z008, Z020-Z026, Z0282, Z0289; and CPT codes 99381-99385, 99391-99395, 99432 99461.
• Dental Visit during WCV: • Dental Exam: ICD-10 Codes Z0120-Z0121, and CDT codes D0120-D0160. • Dental Diagnoses: ICD-10 codes A690, K000-K149, M260-M279, R6884, R859,
Z463-Z464.
• Visit to a Dentist within 365 Days of WCV• Preventive Dental Visit: CDT Codes D1110-D1999
Location
• Office or Hospital• Federally Qualified Health Center• Rural or Public Health Clinic
N = 3,165,865
Office or Hospital
AgeNumber and percent
of WCV VisitsNumber and percent
of Dental ExamsNumber and percentof Dental Diagnoses
0-4 1,177,016 (41%) 63,447 (5.4%) 23,574 (2%)
5-9 714,323 ( 25%) 9,306 ( 1.3%) 8,159 (1.1%)
10-14 605,368 (21%) 7,022 ( 1.2%) 2,669 (0.4%)
15-20 375,415 (13%) 3,662 (1%) 1,347 (0.4%)
FQHC
Age Number and percent WCV VisitsNumber and percent of
Dental ExamsNumber and percent of
Dental Diagnoses
0-4 47,472 ( 36%) 1,681 (3.5%) 758 ( 1.6%)
5-9 33,286 (25%) 295 (0.9%) 699 (2.1)
10-14 31,491 (24%) 237 (0.8%) 268 (0.9%)
15-20 19,032 (14%) 106 (0.6%) 124 (0.7%)
Rural or Public Health Center
Age Number of WCV VisitsNumber of Dental
ExamsNumber of Dental
Diagnoses
0-4 74,171 (46%) 3,316 (4.5%) 1,237 ( 1.7%)
5-9 37,554 (23%) 98 (0.3%) 506 (1.3%)
10-14 30,366 (19%) 40 (0.1%) 132 (0.4%)
15-20 20,371 (13%) 20 (0.1%) 52 (0.3%)
26%
62%59%
46%
38%
66% 64%
55%
40%
58%55%
46%
0-4 5-9 10-14 15-20
Proportion of PDV after WCV at Office/Hospital by age
After WCV in 2016 Had an oral health assessment during a WCV Diagnosed with a dental condition during WCV
27%
57%
43%40%
48%51% 50%
33%
44%
53%48%
36%
0-4 5-9 10-14 15-20
Proportion of PDV after WCV at FQHC by age
After WCV in 2016 Had an oral health assessment during a WCV Diagnosed with a dental condition during WCV
20%
55%
47%
34%
48%
66%
80%
60%
41%
55% 55% 56%
0-4 5-9 10-14 15-20
Proportion of PDV after WCV at Rural/Public Health Clinic by age
After WCV in 2016 Had an oral health assessment during a WCV Diagnosed with a dental condition during WCV
45%
63%
46%
33%
49%
59%
43%47%
44%
59%
44%49%
WHITE HISPANIC BLACK OTHER
Proportion of PDV based on Office/Hospital WCV by Race
After WCV Had an oral health assessment during a WCV Diagnosed with a dental condition during WCV
37%
66%
45%
29%31%
60%
42%
49%48%
56%
43%
49%
WHITE HISPANIC BLACK OTHER
Proportion of PDV based on FQHC WCV by Race
After WCV Had an oral health assessment during a WCV Diagnosed with a dental condition during WCV
26%
66%
45%
21%
37%
60%
41%
54%
35%
63%
44%
35%
WHITE HISPANIC BLACK OTHER
Proportion of Preventive Dental Visits based on Rural or Public Health Clinic WCV by Race
After WCV Had an oral health assessment during a WCV Diagnosed with a dental condition during WCV
Rate of PDV After WCV, by Location
Office/Hospital FQHCRural or Public Health
Clinic
Haz. Ratio S.E. Haz. Ratio S.E. Haz. Ratio S.E.
Race (Reference: White)
Black 1.00 0.00 1.26*** 0.01 1.88*** 0.02
Hispanic 1.61*** 0.00 2.26*** 0.03 3.35*** 0.04
Other 0.82*** 0.00 0.86*** 0.01 1.05*** 0.02
0.613
0
0.1
0.2
0.3
0.4
0.5
0.6
0.71 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106
113
120
127
134
141
148
155
162
169
176
183
190
197
204
211
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225
232
239
246
253
260
267
274
281
288
295
302
309
316
323
330
337
344
351
358
365
Percent of children with a WCV and PDV by Age
0-4 5-9 10-14 15-20
0.4545
0.6304
0
0.1
0.2
0.3
0.4
0.5
0.6
0.71 9 17 25 33 41 49 57 65 73 81 89 97 105
113
121
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137
145
153
161
169
177
185
193
201
209
217
225
233
241
249
257
265
273
281
289
297
305
313
321
329
337
345
353
361
Percent of children with WCV and PDV by Race
White Black Hispanic Other
0.4957
0
0.1
0.2
0.3
0.4
0.5
0.61 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106
113
120
127
134
141
148
155
162
169
176
183
190
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204
211
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232
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246
253
260
267
274
281
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295
302
309
316
323
330
337
344
351
358
365
Percent of children with a WCV and PDV by Dental at WCV
No Dental at WCV Dental Exam at WCV Dental Diagnosis at WCV Both Exam and Diagnosis at WCV
Summary of Results
• Children were mostly seen at an Office/Hospital for WCV• Including all locations, 2.5% of children received a dental exam and
1.3% received a dental diagnoses at WCV• 63% of children between the 5-9 years who had a WCV visited the
dentist within 365 days• 50% of children who received a dental diagnosis and 45% of children
who received a dental exam visited the dentist within 365 days• Hispanic children are attending preventive visits the most and sooner
out of all children
Future Pathways for Interprofessional Practice
• Understand the reasons why the policies are not transformed into practice?
• Higher efforts for medical-dental integration• Education• CE• Practice
QUESTIONS?
6969
Discussion Questions
What can we learn from big data that we couldn’t
learn before?
Why are there obstacles in using EHRs generated
data?
What are the best sources of big data currently
available?
How is research with big data different from what companies like P & R or
Google are doing?
What is the connection between machine
learning/AI and terms like Big Data?
How can we best leverage big data to address key
issues in dentistry, such as interprofessional and
integrated (medical-dental) practice?
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w w w . b s o h s ummi t 2 0 2 0 . c om
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