Converting Electronic Medical Records Data into Practical Analysis Dataset

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1 Converting Electronic Medical Records Data into Practical Analysis Dataset Irena S. Cenzer, University of California San Francisco, San Francisco, CA Mladen Stijacic, San Diego, CA See J. Lee, University of California San Francisco, San Francisco, CA ABSTRACT Electronic medical records (EMRs are becoming an increasingly common source of data for medical research. These records are a rich in medical data, including lab results, medications and health conditions. However, EMR data is usually not set up conveniently for statistical analysis. We describe the methods and SAS code we used to prepare EMR data for two studies in the population of older patients with diabetes. The paper includes examples of using SAS to interpolate values, impute missing values, combine measures from different days into one summary variable, estimating length of stay with discontinued admission and discharge dates and more. INTRODUCTION Electronic medical records (EMR are designed to capture patients medical information quickly and accurately. Those records include data on demographics, medical history, medications, lab test results, etc. Many health care providers have been using EMRs for years and recently enacted regulations provide incentives for all providers to switch to EMRs 1. Electronic records improve patient care enabling easier access to patients medical history and easier communication between different providers. EMRs also create a major opportunity in medical research. The electronic records are databases with vast amount of information on patients that can be used in observational research to determine associations between variables of interest. However, EMRs were not created for the purpose of medical research, and some coding work is necessary to create useful analysis files. In this paper we present SAS code that addresses some of the unique data opportunities and challenges presented by EMRs. STUDIES DESCRIPTION The code presented in this paper was developed for two studies on elderly patients with diabetes. The first study was done using data from On Lok centers. Participants in the On Lok program are NH-eligible, but chose to stay living in the community with help and support from On Lok program. Participants receive health services from On Lok and their medical information is recorded in On Lok electronic records. Each participant receives comprehensive health assessment at the time of enrollment and every three months 2. The goal of the study was to determine the association between levels of Hemoglobin A1C and functional decline. The final analysis dataset included baseline A1C value and baseline functional status, and follow up functional status. The second study was done using data on Veterans Affairs (VA nursing home residents. The VA data includes medical information on veterans receiving medical care through Veterans Health Administration. The data is organized and distributed by VA Information Resource Center (VIReC 3. Our study focused on long-term nursing home residents, with the length of stay of 90 days or longer. The goal was to determine the prevalence of Sliding Scale Insulin (SSI practices in the first month of stay in nursing homes. The final dataset included the indicator variable for presence of evidence of SSI in each day in the first month of patient s stay in VA nursing home. ON LOK DATA AND CODING SCENARIOS On Lok data is organized in multiple files, including demographics, lab results, medications, functional assessment, etc. Each file can contain one or multiple records per patient or no records at all. For example, lab results file will only contain records for the patients that had lab tests performed and it will have one record for each lab test performed. Our study looked at the association between Hemoglobin A1C and decline in functional status, as defined by Activities of Daily Living (ADLs. We used the data from lab results file, functional assessment file and medication file. We created SAS code that generated the final analysis dataset with A1C values as predictors and change in ADLs as the outcome.

2 EXAMPLE 1: A1C measurements are taken at random times Hemoglobin A1c values are entered to the Lab Results file each time they are measured. However, the lab tests are not performed at set time points or at equal time intervals. Physicians usually order lab tests at the time of admission to the On Lok program or at the time when other lab test are ordered. Most commonly, the A1C tests are ordered when the physician suspects that high or low values of A1C are affecting the patient s health status. Therefore, it is possible for patients to have multiple A1C values in a few days period, and then not have another measurement for week or months. We wanted to find a way to estimate A1C value for each patient at any time point during our study period. We interpolated A1C values between any two consecutive A1C measurements using linear interpolation. The code below shows how interpolation was performed in SAS. %macro InsMissDay(MaxLenInt; %let count=0; %do %while (&count <= &MaxLenInt; INSERT INTO MissDay VALUES(&count; %let count=%eval(&count +1; %end; %mend InsMissDay; CREATE TABLE MissDay ( MissDay int; %InsMissDay(1500; CREATE TABLE A1C_INTER AS select a1.id,a1.a1c_date + md.missday AS A1C_DATE, ROUND(case when a2. A1C_DATE =(a1. A1C_DATE + md.missday then a2.a1c else a1.a1c+(md.missday* ( a2.a1c-a1.a1c/(datdif(a1.a1c_date,a2.date_for,'act/act' end, as A1C, from a1c a1 inner join a1c a2 on a1.id = a2.id and a2. A1C_DATE = (select min(a1c_date from a1c where ID=a1.ID and A1C_DATE > a1. A1C_DATE inner join MissDay md ON md.missday <= DATDIF(a1. A1C_DATE,a2. A1C_DATE,'ACT/ACT' UNION select ID, A1C_DATE,a1c from a1c where ID in (select ID from a1c group by ID having count(* = 1 order by ID, A1C_DATE; Output 1. Interpolate Hemoglobin A1C values EXAMPLE 2: Some A1C measurements might not be valid Our study focused on geriatric patients with diabetes. It is unlikely that those patients would have low values of HbA1C (less than 6.5% without medications. Such low untreated A1C measurements were likely data entry errors or errors in diagnosis of diabetes. We wanted to exclude those measurements from our analysis dataset, but did not want to exclude all the measurements for that individual. We excluded only the measurements that were lower than 6.5% without the patient receiving active glycemic control medications at the time. In those cases, the interpolation was done up to the last measurement before the questionable measurement and then discontinued. It was then started again at the next measurement after the questionable measurement. The code below shows how it was performed in SAS. %macro InsMissDay(MaxLenInt; %let count=0; %do %while (&count <= &MaxLenInt; INSERT INTO MissDay VALUES(&count; %let count=%eval(&count +1; %end; %mend InsMissDay; CREATE TABLE MissDay ( MissDay int; %InsMissDay(1500; 2

3 CREATE TABLE A1C_INTER AS select a1.id, a1.a1c_date + md.missday AS a1c_date, ROUND(case when a1.a1c_qsn = 0 and a2.a1c_qsn = 0 then case when a2.a1c_date =(a1.a1c_date + md.missday then a1.a1c else a1.a1c+(md.missday* ( a2.a1c-a1.a1c/(datdif(a1.a1c_date,a2.a1c_date,'act/act' end when a1.a1c_qsn = 0 and a2.a1c_qsn = 1 then case when a1.a1c_date =(a1.a1c_date + md.missday then a1.a1c else. end else. end, as A1C from a1c_meds a1 inner join a1c_meds a2 on a1.id = a2.id and a2.a1c_date = (select min(a1c_date from a1c_meds where ID=a1.ID and a1c_date> a1.a1c_date and (a1.a1c_date >= (select min(a1c_date from a1c_meds where ID = a1.id and a1c_qsn =0 and a2.a1c_date <= (select max(a1c_date from a1c_meds where ID = a1.id and a1c_qsn =0 inner join MissDay md ON (md.missday < DATDIF(a1.a1c_date,a2.a1c_date,'ACT/ACT' or a2.a1c_date is missing UNION select ID, a1c_date, a1c from a1c_meds a1 where a1c_date = (select max(a1c_date from a1c_meds where ID = a1.id and a1c_qsn =0 UNION select ID, a1c_date, a1c from a1c_meds where ID in (select ID from a1c_meds group by ID having count(* = 1 and a1c_qsn = 0 order by ID,a1c_date; Output 2. Interpolate Hemoglobin A1C values and exclude the questionable A1C values EXAMPLE 3: Calculate summary ADL value On Lok staff performs functional status evaluation every three months. During the evaluation they collect data on five Activities of Daily Living: bathing, dressing, toileting, transferring in/out of bed and eating. For each ADL, patients are assigned a value of 0 (no difficulty, 1 (difficulty or 2 (dependence. Most of the time all the ADLs are evaluated at the same day, but sometimes one or more ADLs have to be assessed one or more days later. We considered the ADLs as assessed at the same time if they were evaluated within two weeks of each other. The code below shows how to combine ADLs from different days with two weeks into one summary variable. create table ADL_summary as select x.id, x.effective_date, sum(adl.adl_type as sum_of_adl_types, count(adl.adl_type as count_of_adl, sum(adl.value as score_sum_miss, round(mean(adl.value as score_avg from ADL inner join (select * from ADL where ADL_type=1 x on ADLfreqs.effective_date >= (x.effective_date -14 and ADL.effective_date <= (x.effective_date + 14 and ADL.ID = x.id group by x.id,x.effective_date ; run; Output 3. Calculate summary ADL value Sometimes it is not possible to evaluate one or more activities for a subject, but we do not want to exclude that subject from the study completely. In those cases, we imputed the average value score of the ADLs that are not missing for the ADLs that are missing. data ADL_summary; set ADL_summary; score_sum = score_sum_miss + (5-count_of_adl*score_avg; run; Output 4. Impute missing ADL values and calculate summary ADL value 3

4 EXAMPLE 4: Determining the baseline ADL and the six month follow up ADL As mentioned, the goal of our study was to test the association between the current A1C value and the change in ADLs over six months. In order to determine if a patient experienced decline in ADL status, we first need to determine the baseline and the follow up functional status. Since functional assessments are not performed exactly every 180 days, we defined our six month follow up ADL as the ADL assessment that was done closest to 180 days from the baseline ADL, but not more than 30 days away from 180 day mark. The code below shows how we defined the baseline and follow up ADLs. create table ADL_followup as select adl_first.id,adl_first.effective_date as FIRST_DATE, adl_first.score_sum as FIRST_ADL, adl_second.effective_date as SECOND_DATE,adl_second.score_sum as SECOND_ADL from ADL_summary adl_first LEFT OUTER JOIN ADL_summary adl_second ON adl_first.id = adl_second.id and (adl_first.effective_date+150 <= adl_second.effective_date and (adl_first.effective_date+210 >= adl_second.effective_date and abs((adl_first.effective_date+180-(adl_second.effective_date = (select min(abs((x1.effective_date+180-(x2.effective_date from ADL_summary x1 inner join ADL_summary x2 on x1.id = x2.id and x1.id = adl_first.id and x1.effective_date = adl_first.effective_date and adl_first.effective_date+150 <= x2.effective_date and adl_first.effective_date+210 >= x2.effective_date; run; Output 5. Calculate baseline and follow up ADL values VA NURSING HOME DATA AND CODING SCENARIOS VA data is organized in multiple files, including main files, lab files, medication files, etc. Similar to On Lok data, each file can contain one or multiple records per patient or no records at all. In this study we used Extended Care Main (ECM file and Lab Results (LR file. We created SAS code that generated the final analysis dataset that included day of stay and incidence of SSI. EXAMPLE 1: Calculating total length of stay The information about admission to and discharge from the VA nursing home is found in Extended Care Main file. If patient gets transferred from nursing home to another inpatient or outpatient setting for tests or treatment, he is still considered nursing home resident and his bed is not released. However, in the administrative data it will appear that the patient was discharged from the nursing home and then admitted again the same day. Each record in the Extended Care Main file describes one admission to nursing home. Therefore, it is possible that a patient will have multiple records in the main ECM file for the same stay in the nursing home. This would complicate the process of calculating the length of stay in nursing home. The code below describes how to combine multiple admission and discharge dates for each stay and easily calculate the total length of stay in the nursing home. select ad.id, ad.admitday, (select min(disdayfrom ext_care_main dd where dd.disday >= ad.admitday and dd.id = ad.id and not exists (select * from ext_care_main nad where nad.admitday = dd.disday and nad.disday > dd.disday and nad.id = dd.id format date8. AS disday from ext_care_main ad where not exists (select * from ext_care_main pdd where pdd.disday = ad.admitday and pdd.admitday < ad.admitday and ad.id = pdd.id order by ad.id,ad.admitday; Output 6. Calculate Length of Stay in nursing home 4

5 EXAMPLE 2: Determine Sliding Scale Insulin Our research question was focused on prevalence of Sliding Scale Insulin in the first month of stay in nursing homes. SSI is identified by multiple insulin medications or multiple glucose checks in the same day. The code below shows how identify multiple insulin medications in the same day. The same logic is applied to identifying multiple blood sugar tests in the same day. (Dataset day_inc includes one line per each day of interest, 0-30 in this case. proc sql ; create table temp AS select x.id, x.admit_date, x.discharge_date, x.insulin_date, case when x.admit_date + di.inc = x.insulin_date then x.prepcount else 0 end as PrescriptionCount, 'Day' btrim(put(di.inc+1,2. '_NumIns' as InsDay, di.inc from day_inc di inner join (select i.id,i.admit_date,i.discharge_date,p.insulin_date,p.prepcount from inpatient i inner join (select id,insulin_date,count(* AS PrepCount from Pharmacy group by id,insulin_date p on i.id=p.id and i.admit_date <= p.insulin_date and p.insulin_date <= i.discharge_date x on di.inc < 28 and x.insulin_date = x.admit_date + di.inc; proc sql ; CREATE TABLE temp1 AS select di.inc,t.inc as incins,t.id,t.admit_date,t.discharge_date, case when t.inc=di.inc then t.prescriptioncount else 0 end as PrescriptionCount, case when t.inc=di.inc then t.insday else 'Day' btrim(put(di.inc+1,2. '_NumIns' end as InsDay from id_temp di inner join temp t on t.id = di.id and t.admit_date=di.admit_date and ((di.inc >= t.inc and di.inc < (select min(inc from temp where id = t.id and admit_date=t.admit_date and inc > t.inc or (t.inc = (select max(inc from temp where id = t.id and admit_date=t.admit_date and di.inc >= t.inc or (t.inc = (select min(inc from temp where id = t.id and admit_date=t.admit_date and di.inc < t.inc where missing(t.id = 0 order by t.id,t.admit_date,di.inc; proc transpose data=temp1 out = LOS; id Insday; by id admit_date; var PrescriptionCount; Output 7. Determine occurrence of Sliding Scale Insulin CONCLUSION Information collected in Electronic Medical Records can a great resource in medical research. Unfortunately, data is not collected and stored in a way that makes it immediately useful for research. In this paper we presented a few examples of how to make EMR data suitable for use in statistical analysis. The code included here and additional code can be obtained by contacting the authors. REFERENCES 1. Centers for Medicare & Medicaid Services, Guidance/Legislation/EHRIncentivePrograms/Meaningful_Use.html 2. Eng C, Pedulla J, Eleazer GP et al. Program of All-inclusive Care for the Elderly (PACE: An innovative model of integrated geriatric care and financing. J Am Geriatr Soc 1997;45: VA Information Resource Center, 5

6 CONTACT INFORMATION Your comments and questions are valued and encouraged. Contact the author at: Name: Irena Stijacic Cenzer Enterprise: UCSF Address: 4150 Clement St. City, State ZIP: San Francisco, CA Work Phone: ( SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. Other brand and product names are trademarks of their respective companies. 6