Improving Estimates for Electronic Health Record Take up in Ohio: A Small Area Estimation Technique

Daniel Weston, M.B.A.The Ohio Colleges of Medicine: Government Resource Center

Background Details on the Electronic Health Records

Survey Initial Estimates Regression Models Bootstrap Methods Conclusions Recommendations for the next survey and

future work

Outline

To estimate the EHR take up rates for Ohio◦ Medical practitioners, primary care physicians,

medical specialists, dentists, nurse practitioners, and nurse midwives with a Medicaid patient volume above 30%, and pediatricians with a Medicaid patient volume above 20%.

The motivation for these estimates is that every practitioner above the required volume thresholds will be eligible to apply for financial assistance for the adoption and implementation of an EHR system

Background

The data used for this thesis come from the 2010 Ohio Electronic Health Records Survey (EHRS).

To determine Electronic Health Records (EHR) take up by Ohio medical practitioners enrolled as Medicaid providers.

To help reveal key barriers to EHR adoption.

Estimate the proportion of EHR adoption by 2015.

Details on the Electronic Health Records Survey

To all known fax numbers among sample practices.

To encourage response, a blanket approach was employed by faxing all non-responding medical practices the cover letter and instrument to the office manager.

Faxing and return by fax vastly improved total response rates in two days.

19.23% Response rate

Possible problems◦ Non response Bias◦ Surveyed practices not practitioners

Follow-up faxing approach

Initial Stratified Estimates Sahr et al. (2010)

       

Professional type

Estimated Total Number of

Practitioners Have Purchased and or installed

an EHR

    Number PercentPrimary Care 3,639 1,721 47.29%Pediatrics 1,303 591 45.35%Physician Specialist 4,058 1,949 48.03%Dentist 1,098 205 18.70%Nurse Practitioner/ Nurse Mid Wife

398 189 47.62%

Total 10,496 4,655  44.35%Practices surveyed from Ohio Medicaid records with 200 or more Medicaid patients.

No confidence interval reported Assumed practitioner per practice was constant

across the state County-level estimates not provided

Obtained county attributes from the 2010 Census data

Obtained county-level health attributes from the Ohio Family Health Survey (OFHS)

Fit a linear regression model using responses from the EHRS, Census Data, and the OFHS to determine a county-level estimate of a physician’s probability for EHR take up;

Fit a linear regression model using responses from the EHRS, Census Data, and the OFHS to determine a county-level estimate of doctors per practice; and

Using these estimates, the number of doctors per practice per county was estimated and new estimates for total EHR take up were determined.

Small Area Estimation

Small Area Estimation

is the px1 vector of regression coefficients for the auxiliary data

is the px1 vector of regression coefficients for the variables used in the EHRS

is the intercept term

are area-specific random errors assumed to be independent and identically normally distributed with mean = 0 and variance ≥ 0

Regression Model 1: Log Sum of Practice Size

logsumpracsize = 0.000110 (veterans) + 14.2 (Medicaid) - 0.000016 (Black_population)+ 11.6 (Self_rated_health_%_cnty)+ 8.80 (%_County_unmethealthcare) -0.000047 (Median_household_inc)+ 1.28 (%_practice_in_cnty_responding_early) - 9.70 (%_County_poverty) + 0.912(%_prac_in_county_ ehr)- 7.92

Predictor Coef SE Coef T P

Constant -7.915 3.408 -2.32 0.024

Veterans - total 2005-2009 0.00010997 0.00002053 5.36 .000

% of County on Medicaid 14.163 4.387 3.23 0.002

Total black population of the county -0.0000165 0.00000593 -2.78 0.007

Self rated health status (Good/Excellent=1 or Fair/Poor=0) 11.632 3.75 3.1 0.003

% County an unmet health care need 8.797 3.58 2.46 0.017

Median household income 2009 in dollars -0.00004688 0.0000176 -2.66 0.01

% of county responded early to the EHRS 1.277 0.587 2.18 0.034

% of County Below 100% Federal Poverty Level -9.696 5.856 -1.66 0.103

% County with EHR 0.9123 0.613 1.49 0.142

S = 0.959885 R-Sq = 68.1% R-Sq(adj) = 63.2%    

Regression Model 1: Log Sum of Practice Size

The parameter estimates suggest that the log in number of practitioners increases with: ◦ The number veterans in a county◦ A higher percent of Medicaid recipients in the county◦ Higher proportion of the county with excellent or good self-rated-

health-status◦ A higher proportion of county practitioners responding to the EHRS

early◦ And a higher proportion of the county with an unmet-health-care-

need

It decreases with◦ The number of black individuals in the county◦ The median home price in the county◦ Proportion of people below 100% FPL

Regression Model 1: Log Sum of Practice Size

logehruptake = - 2.40 - 0.00308 (Land total square miles)+ 0.00000005 (Wholesale trade sales)+ 0.000004 ( total farmable acres)+ 0.00970 practice size + 0.000085 (Per capita income )+ 2.90 (% of cnty unmethealthcare)- 0.0925 (Housing change)+ 0.0734 (population,percent change )- 0.154 (AGE u5)+ 0.131 (CLF) - 0.787 (er_mean)

Regression Model 2: Log EHR Status

Predictor Coef SE Coef T P

Constant -2.399 1.08 -2.22 0.03

Land total square miles -0.0030779 0.0006144 -5.01 .000

Wholesale trade: merchant wholesalers sales of establishments with payroll 0.00000005 0.00000002 3.07 0.003

Land in total farmable acres 0.00000388 0.00000095 4.08 .000

Reported size of practice in the EHRS 0.009701 0.004558 2.13 0.038

Per capita income in the past 12 months (in 2009 inflation-adjusted dollars) 0.0000851 0.00003097 2.75 0.008

Percent of county with an unmet health care need in 2007-08 from the OFHS 2.903 1.462 1.99 0.052

Housing unit estimates - percent change, April 1, 2000 (base) to July 1, 2009 -0.09247 0.02527 -3.66 0.001

Resident total population, percent change - April 1, 2000 to April 1, 2010 0.07339 0.01588 4.62 .000

Resident population under 5 years, percent -0.15444 0.09187 -1.68 0.098

Civilian labor force unemployment rate 2009 0.13069 0.03294 3.97 .000

County mean number of visits to the Emergency Room in from the OFHS -0.7867 0.4122 -1.91 0.061

S = 0.398927 R-Sq = 58.4% R-Sq(adj) = 50.3%

Regression Model 2: Log EHR Status

The parameter estimates suggest that the log in EHR take up increases with: ◦ Total sales of wholesale trade◦ Farmable acres◦ Larger average doctor practice group sizes◦ Per capita income◦ Higher county percent of unmet healthcare needs◦ County population◦ And the size of the civilian labor force

Log EHR decreases with:◦ Total square miles in county◦ Housing change increases◦ Total number of children five years old or under◦ And average emergency room visits per county

Regression Model 2: Log EHR Status

CountyEHR Take up

EstimateDPPPC

EHR Take up*

DPPPCDPPPC Floor 1

EHR Take up*

DPPPC Floor 1

Total

Practices in

County

Estimated

Practitioners with

an EHR per County

Estimated

Practitioners with

an EHR per County

F1

Adams 42.82% 0.2235 0.0957 1.0000 0.4282 19 2 8

Allen 38.91% 2.2700 0.8833 2.2700 0.8833 115 102 102

Ashland 30.08% 1.5276 0.4596 1.5276 0.4596 34 16 16

Ashtabula 16.48% 5.3863 0.8877 5.3863 0.8877 64 57 57

State-wide Estimate and problems

Wyandot 48.68% 1.9085 0.9291 1.9085 0.9291 8 7 7

Total 21,822 21,847

Two major flaws of the survey instrument are revealed No way to decipher how many of the practitioners serving in a group practice are above the MPIP thresholds individually. And secondly, there is no way to decipher if a practitioner was reported by multiple practices

We will approximate the number of eligible MPIP applicants at 25% of the estimated total number of practitioners with an EHR working in practices serving over 200 Medicaid recipients

The estimated total numbers of practitioners with an EHR who are working in practices serving over 200 Medicaid recipients over all counties is 5,455 estimated cost $347,756,250

95% confidence interval for the number of practitioners working in offices serving 200 or more Medicaid patients is (0.00066, 613449633044.38)◦ We gain nothing here

State-wide Estimate and problems

Bootstrapping is a technique that treats the original county data used to fit Models 1 and 2 as a pseudopopulation and samples counties randomly with replacement (srs wr) m=999 times.

We use these random subsamples of counties and their corresponding data points are used to re-fit our Models 1 and 2

Non Parametric Bootstrap

Non Parametric Bootstrap

Min. Median Estimated Cost 95% LCL 95% UCL 80% LCL 80% UCL

Practitioners in practices that

serve over 200 Medicaid

patients using an EHR and

above the MPIP threshold

286 4,566 $291,082,500 635 47,652 1,138 18,043

Non Parametric Bootstrapping was less sensitive to extreme outliers

Recommendations for future work

The instrument should ask for National Provider Identification (NPI) numbers of all practitioners accounted for in the practice size

The instrument should include a question which will allow researchers to determine full time equivalents for practitioners.

The instrument should ask Medicaid volume for each separate NPI number and obtain the specialties of each individual practitioner.

The instrument should clearly ask if all NPI numbers listed fully use the EHR system, if not which do not.

The instrument should ask questions relating to the effectiveness of the EHR if on is installed.

If at all possible the EHRS should be done at the practitioner level to avoid responses from practitioners not in the sampling frame. This could be done by sampling from the NPI list instead of the Medicaid Provider list.

Conclusions The goal of this thesis was to accurately estimate the EHR take

up rates for Ohio’s medical practitioners (with a Medicaid patient volume above 30%, and pediatricians with a Medicaid patient volume above 20%) using Small Area Estimation.

Small Area Estimation combined data from the EHRS, OFHS, and the 2010 Census and, using linear regression, estimated that 5,455 practitioners were eligible for MPIP funding. SAE’s large variance produced a wide 95% confidence interval was which is not very useful (bounded by zero and the estimated number of practitioners licensed to practice in Ohio (0, 41964)).

Employing the non-parametric bootstrap method, we obtained a new estimate for practitioners eligible for MPIP funding to be 4,566, with a 95% confidence interval of (635, 47,652). Lower confidence bounds were calculated to establish a minimum for Ohio to budget for the MPIP

Conclusions Although our models are based on imperfect data, Small Area

Estimation is the preferred technique, because it provides county-level estimates for all counties, (even for counties with no responses from the EHRS – although caution must be used with these estimates for counties without representation in the EHRS)

Non-parametric Bootstrapping with Small Area Estimation is also recommended in future iterations of the EHRS because is less sensitive to outliers.

The non-parametric bootstrap allowed us to calculate lower confidence bounds, allowing a baseline of MPIP eligibility to be established for budgetary purposes which the original estimates did not allow.

Therefore, this thesis has shown Small Area Estimation with bootstrapping the preferred technique to the original method used by Sahr et al. (2010).

Ohio Department of Job and Family Services/ Ohio Medicaid

Dr. Elizabeth Stasny, The Ohio State University Department of Statistics

Dr. Eloise Kaizar, The Ohio State University Department of Statistics

Timothy R. Sahr, The Ohio Colleges of Medicine, Government Resource Center, The Ohio State University Office of Health Sciences

Lorin Ranbom, The Ohio Colleges of Medicine, Government Resource Center, The Ohio State University Office of Health Sciences

Acknowledgements