Executive Summary f Ppulatin Health Management Reprt Time Perid: Medical Utilizatin Data: January 1, 2013 t December 31, 2014 Pharmacy Utilizatin Data: January 1, 2013 t December 31, 2014 Prepared fr: State f Arkansas Bureau f Legislative Research 2015 Human Factr Analytics, Inc. Executive Summary
Executive Summary Intrductin The fllwing reprt is the result f an analysis f archival medical and pharmacy utilizatin data fr Arkansas State Emplyee (i.e., labeled as ASE ) and Public Schl Emplyee (i.e., labeled as PSE ) health plans that service emplyees, spuses, dependents, and retirees f the State f Arkansas. The intent f this analysis is t yield a better understanding f the epidemilgy currently influencing this ppulatin and t suggest ppulatin health management pprtunities that can address the specific risk impacting this ppulatin. In rder t accmplish this task, archival data was prcessed thrugh prprietary algrithms in rder t prperly risk-stratify the ppulatin. The risk f a ppulatin has a direct relatinship t current and future spending patterns. Variables that are the building blcks f risk and/r disease include, but are nt limited t: Age, Gender, Lifestyle, Genetics, Ethnicity, Acute Illness, Chrnic Illness, C-Mrbidities, Multi-Mrbidities, Medicatin Cmpliance/Nn-Cmpliance, Cmpliance/Nn-Cmpliance t Evidence-Based Guidelines, Gaps in Care, etc. The majrity f the afrementined variables were utilized t investigate risk stratificatins within the ppulatin. A sample size f this magnitude can yield unique insights int future ppulatin health management strategies. The verall health f a ppulatin is determined by multiple factrs; hwever, an individual s lifestyle is a pwerful predictr f leading causes f mrbidity and disability. This reprt has sme limitatins in that lifestyle factrs such as physical activity status, nutritin, tbacc use, and weight/bmi culd nt be included in the stratificatins f risk assciated with this ppulatin. Hwever, if the Arkansas State & Public Schl Life & Health Insurance Prgram Legislative Task Frce and the Bureau f Legislative Research decide t mve frward with recmmended ppulatin health management strategies, this data can be cllected and included in future analyses. This analysis explred multiple areas f interest within the data, including the fllwing research questins: 1. What is the cst burden f lifestyle mdifiable risk factrs within the emplyee ppulatin? 2. What is the relatinship f age and gender t varius disease states? 3. What are the gaps in care assciated with suggested preventive measures fr this ppulatin? 4. What is the relatinship between drug cmpliance and nn-cmpliance, as related t disease severity? 5. What is the financial burden assciated with chrnic disease within this ppulatin? 6. What is the distributin f acute disease versus chrnic disease within this ppulatin? 7. What is the level f HEDIS cmpliance (i.e., evidence-based & preventive medicine) within this ppulatin? 8. What is the expense related t specific c-mrbidities (i.e., hypertensin, hyperlipidemia, depressin, etc.) within this ppulatin? 9. What variables best predict and explain future high spenders within this ppulatin? 10. What are actinable slutins that can be implemented t mitigate existing and future health risks? 2015 Human Factr Analytics, Inc. Executive Summary Page 2
This reprt has attempted t explain the causality f risk and precursrs t risk within the State f Arkansas ASE and PSE data. As was validated thrugh this analysis, there is a wide variety f risk that was identified thrugh the archival healthcare utilizatin and pharmacy data. It shuld be nted that each risk grup ffers an pprtunity fr ppulatin health management strategies. Sme f these strategies will include therapeutic lifestyle change (e.g., exercise, prper nutritin, weight management, tbacc cessatin, etc.) and sme f the strategies will include specific evidence-based clinical tasks. Successful ppulatin health management interventins are well cmmunicated, sensitive t human behavir patterns, and are implemented int a supprtive wrk envirnment. Ppulatin health management has been implemented in the United States fr mre than 30 years. Scientific dcumentatin has prven that well-designed prgrams can yield reductins f risk within the participating ppulatin and ptential reductins in medical expenditures. Thrugh the use f analytics, pre and pst results frm a ppulatin health management prgram can be measured and strategies can be amended t ensure prgram success. By having access t additinal data, many mre questins can be explred with regard t this ppulatin. Our hpe is that this reprt will stimulate the need fr further questining f the data and the start t a successful risk management strategy. Key Findings and Slutins fr Cnsideratin The fllwing key findings resulted frm the analysis f archival health care data (i.e., medical utilizatin data and pharmacy utilizatin data) cnducted by Human Factr Analytics. Key Finding 1: Reductins in Spending frm 2013 t 2014 Pages 14-15, 24-27, 32-35, 53-62, and 65-69 f Ppulatin Health Management Reprt Key Finding: When lking at verall spending fr the ASE and PSE ppulatins cmbined, there was a $19,778,382 reductin in medical spending frm 2013 t 2014; this dllar figure was based n ttal amunt paid. Bth ppulatins als had a slight reductin in mean (average) expenditures frm 2013 t 2014; the PSE ppulatin had a mean expenditure f $2,542 in 2013 and a mean expenditure f $2,261 in 2014. The ASE ppulatin had mean expenditures f $2,786 in 2013 and $2,586 in 2014. Savings was als realized in pharmacy expenditures; this savings cmbined fr PSE and ASE was $28,707,079. This savings was primarily due t the inclusin f reference-based pricing fr several drug categries and ther cnsumer-based strategies (i.e., a large prtin f the ppulatin was taking generic and therapeutic equivalent medicatins rather than brand name medicatins). An analysis was cnducted t investigate the causality f the reductin in medical spend (Refer t Attachment 3). The analysis first lked at the verall state f health f the ppulatin t see if the ppulatin was healthier frm 2013 t 2014 r if there had been sme type f universal risk reductin. Several methdlgies were used t quantify risk within the ASE and PSE ppulatins frm 2013 t 2014. Patterns f risk generally ccur within any given ppulatin. In rder t better understand these patterns, the ppulatin was risk stratified int the fllwing five distinct grups: Grup Descriptin 1 N chrnic disease and less than $1,500 utilizatin expenditures per 12 mnths 2 N chrnic disease and $1,500 r mre utilizatin expenditures per 12 mnths 3 Chrnic disease* with n c-mrbidities and n cmplicatins 4 Chrnic disease with c-mrbidities, but n cmplicatins 5 Chrnic disease with c-mrbidities and disease-specific cmplicatins**, r chrnic disease with disease-specific cmplicatins but n c-mrbidities *This calculatin includes the fllwing chrnic diseases: Asthma, Cancer, Heart Disease, Hypertensin, COPD, Diabetes, Obesity, Hyperlipidemia, and Depressin. **This calculatin includes cmplicatins t the fllwing diseases: Asthma, Diabetes, COPD, and Heart Disease. 2015 Human Factr Analytics, Inc. Executive Summary Page 3
Mean amunt paid within the ASE ppulatin was as fllws in 2014: Grup 1: N = 29,582 Mean = $372 Grup 2: N = 5,751 Mean = $5,603 Grup 3: N = 16,086 Mean = $2,783 Grup 4: N = 13,920 Mean = $4,123 Grup 5: N = 3,325 Mean = $9,375 Mean amunt paid within the PSE ppulatin was as fllws in 2014: Grup 1: N = 44,849 Mean = $299 Grup 2: N = 6,643 Mean = $6,323 Grup 3: N = 20,482 Mean = $2,622 Grup 4: N = 14,120 Mean = $4,180 Grup 5: N = 2,868 Mean = $10,937 An analysis was cmpleted t investigate the ecnmic differences between each grup. The analysis revealed that fr bth ASE and PSE ppulatins, mean expenditures increased as an individual incrementally prgressed frm Grup 3 t 4 t 5. It shuld be nted that in chrnic Disease Grups 3, 4, and 5, spending was $7,551,838 less fr the ASE ppulatin in 2014 when cmpared t 2013 spending. The ttal number f chrnic diagnses slightly increased fr Grup 3 and Grup 4. Even thugh the numbers increased, verall spending decreased fr these grups. When lking at the PSE ppulatin, spending als reduced in Grups 3 and 4 and was slightly higher fr Grup 5. Even after subtracting the added csts that Grup 5 had in 2014, there was still an $8,315,974 dllar reductin in spending. When bth the reductin in spending fr the ASE and the PSE ppulatins are added tgether, that equates t a reductin in spending related t the ppulatin with chrnic disease (i.e., Grups 3, 4, and 5) f $15,867,812. Therefre, it is plausible t suggest that this reduced spending was due t the increased preventive visits that tk place between 2013 and (primarily) in 2014. Past research studies have demnstrated that varius preventive visits can lead t cst reductins f 8 t 9 percent (cited research is available upn request). In rder t better validate this bservatin, 2012 data shuld be analyzed as a baseline year and ther statistical experimentatin shuld be cmpleted. In 2013 and 2014 cmbined, there were a ttal f 25,011 individuals frm the ASE ppulatin wh had preventive health cdes (i.e., cdes that were included in the wellness prgram, as listed in Appendix V) and 45,535 individuals frm the PSE ppulatin wh had preventive visits. In rder t test if participants were f equal risk status t nn-participants, an analysis was cnducted that cunted the number f unique diagnses fr each grup t ascertain the equality f risk (Refer t Attachment 2). The greater the number f ICD-9 cdes, the greater the risk. In additin t the analysis f risk equality, an analysis was perfrmed t islate utcmes derived frm individuals underging a clnscpy as a preventive visit (Refer t Attachment 6). The results identified 1,152 unique individuals frm PSE ppulatin wh had a cln cancer screening and had a tumr r plyp bipsied r remved; the analysis further identified 42 unique individuals with a diagnsis f cln cancer. Fr the ASE ppulatin, 967 unique individuals had a tumr r plyp bipsied r remved, and 31 unique individuals had a diagnsis f cln cancer. The early diagnsis f cln cancer can greatly reduce cst f treatment, imprve clinical utcmes, and cntribute t an individual s quality f life. The strategy t increase preventive visits seems t have yielded sme gd utcmes fr bth the ASE and PSE ppulatins. 2015 Human Factr Analytics, Inc. Executive Summary Page 4
Based n the chrnic diseases included in the afrementined Disease Grup Risk Stratificatin, mre than 45 percent f the ASE ppulatin and mre than 40 percent f the PSE ppulatin (i.e., f the prtin f each ppulatin that had medical claims in 2014) had a chrnic disease. It wuld be estimated that an additinal 10 t 15 percent f the ppulatin have chrnic illness and have nt yet been diagnsed, due t gaps in care. The tp three mst expensive chrnic diseases fr bth the ASE and PSE ppulatins in 2014 were: (1) Cancer, (2) Heart Disease, and (3) Diabetes. The tp three mst frequently diagnsed chrnic diseases fr the ASE ppulatin in 2014 were: (1) Hypertensin, (2) Hyperlipidemia, and (3) Cancer. The tp three mst frequently diagnsed chrnic diseases fr the PSE ppulatin in 2014 were: (1) Hypertensin, (2) Cancer, and (3) Hyperlipidemia. Fr bth the ASE and PSE ppulatins, Diabetes was number three (3) fr verall csts and number fur (4) fr frequency. It shuld be nted that Diabetes is ften a precursr fr Heart Disease, Renal Disease, and Cancer. An analysis was perfrmed t lk at the prevalence f catastrphic expenditures fr 2013 and 2014 (Refer t Attachment 4). Catastrphic spend was defined as individuals claims exceeding $100,000. The ASE ppulatin had 52 claims in 2013 and 59 claims in 2014. The PSE ppulatin had 85 claims in 2013 and 98 claims in 2014. Thus, bth grups had increased catastrphic claims frm 2013 t 2014. Recmmended Slutin: The impact f chrnic disease, c-mrbidities, and disease-specific cmplicatins magnifies the impact f an individual s mean and verall expenditures. This type f stratificatin (i.e., the afrementined Disease Grup Risk Stratificatin) clearly shws that a relatively similar grup f individuals drives a large percentage f verall expenditures. A ppulatin health management strategy that targeted individuals in Grups 1, 2, & 3 wuld have the largest return n investment. Grups 1, 2, and 3 wuld be cnsidered emerging risk r lw risk ppulatins. The challenge is t prevent individuals with chrnic disease frm develping c-mrbidities and disease-specific cmplicatins. Special attentin shuld be given t evidence-based medicine cmpliance fr individuals with chrnic disease in rder t prevent migratin t higher risk status. This, in cmbinatin with lifestyle mdificatin, shuld be a primary fcus fr future ppulatin health management strategies. Cnsider the implementatin f a health risk appraisal and bimetric screenings (i.e., height, weight, Bld Pressure, Ttal Chlesterl, LDL Chlesterl, HDL Chlesterl, Triglycerides, Glucse, HbA1c) fr the insured lives within the health plan. A screening f this type will yield invaluable data, increase health risk awareness, and identify individuals that are currently undiagnsed with chrnic illness. Implement a Cultural Audit t determine the ppulatin s receptivity t a ppulatin health management prgram. The Cultural Audit will identify critical viewpints frm management-level persnnel versus nn-management persnnel. This type f audit can yield valuable infrmatin t the planning stage f any ppulatin health management initiative. Intrduce a participatin-based wellness prgram in Year 1. A participatin-based wellness prgram allws an emplyer t cnnect wellness participatin (e.g., cmplete a Health Risk Appraisal and participate in a Bimetric Screening) with an emplyer-spnsred health plan. Cnnecting the wellness prgram with incentives thrugh the health benefits plan will help ensure high participatin rates amng plan participants. The data captured thrugh the wellness prgram will help with the early identificatin f individuals with varius chrnic diseases (e.g., hypertensin, diabetes, hyperlipidemia, besity, metablic syndrme, etc.) and help cnnect these individuals with physicians fr clinical attentin t their varius risk factrs. It wuld be expected that a prgram f this type wuld identify an additinal 10 t 15 percent f the 2015 Human Factr Analytics, Inc. Executive Summary Page 5
ppulatin with chrnic illness. The bimetric screening shuld include Height, Weight, Bld Pressure, Ttal Chlesterl, HDL Chlesterl, LDL Chlesterl, VLDL Chlesterl, Triglycerides, Glucse, HbA1c, and Girth Measurement. Cnsider the use f a Health Risk Appraisal (HRA) that has actuarial validity with regard t predicting high-spend individuals. Thrugh the use f advanced analytics a crrelatin can be made between an individual s verall HRA scre and their verall and mean health care expenditures. In the future, this relatinship culd aid State f Arkansas in negtiating insurance rates (i.e., re-insurance, disability, and life insurance) and better prject future expenditures. In Year 2 f the interventin, cnsider evlving the participatin-based wellness prgram int a strategy that utilizes evidence-based clinical rules t guide participants t chse frm a menu f clinical t ds that are relevant t the participant s age, gender, health status (i.e., chrnic versus nn-chrnic) and gaps in care. Fr example, if the participant has chrnic disease, give incentive fr the participant t take their medicatins and get their disease-specific preventive visits. An analysis was cnducted t demnstrate the value f individuals with diabetes cmplying with their medicatins; the analysis revealed that cmpliance t evidence-based medicatins fr diabetes reduced the chance f develping diabetes-specific cmplicatins (Refer t Attachment 7). Based n an additinal analysis, there were a large number f individuals with a diagnsis f diabetes within the ASE and PSE ppulatins wh are nn-cmpliant t evidencebased medicatins related t diabetes management (Refer t Attachment 1). Systems are available that can mail specific clinical t ds t each member s hme and mnitr n-ging cmpliance t these directins; this strategy als impacts the spuse and dependent children. The majrity f wellness prgram strategies ften d nt implement prgrams that are sensitive t the clinical side f ppulatin health management and just cncentrate n lifestyle mdificatin (e.g., exercise, nutritin, stress management, etc.). Hwever, in rder t be effective with the chrnic ppulatin, clinical strategies must be a part f the verall ppulatin health management strategy. Further analyses were cnducted t identify the imprtance f chrnic disease as a predictr f future spending (Refer t Attachments 8 and 9). Key Finding 2: Diabetes Cmplicatins and C-Mrbidities Pages 28-29 f Ppulatin Health Management Reprt Key Finding: The tp three Diabetes-specific cmplicatins fr bth the ASE and PSE ppulatins in 2014 were: (1) Cardivascular, (2) Neurpathy, and (3) Retinpathy. Diabetesspecific cmplicatins are assciated with uncntrlled diabetes and smetimes with undiagnsed diabetes. Fr example, a diagnsis f Idipathic Neurpathy means f n knwn cause ; hwever, it is ften assciated with an undiagnsed case f diabetes. Wellness prgramming that includes bimetric screenings wuld identify individuals with undiagnsed diabetes. Individuals with diabetes were identified and a risk stratificatin analysis was perfrmed. The results f this stratificatin discvered that fr the ASE ppulatin in 2014 there were 2,122 individuals with diabetes that had nly 0 t 1 c-mrbidities attached t their primary diagnsis f diabetes. Fr the PSE ppulatin in 2014, there were 2,344 individuals with diabetes that had nly 0 t 1 c-mrbidities attached t their primary diagnsis f diabetes. Disease management in cmbinatin with cmpliance t HEDIS guidelines fr diabetes wuld ffer a high return n investment with this grup f emerging and lw-risk individuals with diabetes. Recmmended Slutin: Establish evidence-based medicine guidelines (i.e., HEDIS gals, as described in the Recmmended Slutin fr Key Finding 3) fr the ppulatin that relate t diabetes management: 2015 Human Factr Analytics, Inc. Executive Summary Page 6
Hemglbin A1c (HbA1c) testing Hemglbin A1c cntrl (<7.0%) Retinal eye exam perfrmed LDL-C screening LDL-C cntrl (<100mg/dl) Screening fr neurpathy Bld Pressure cntrl (<130/80 mm/hg) Medical attentin fr nephrpathy Key Finding 3: Preventive Screenings Pages 41-42 f Ppulatin Health Management Reprt Key Finding: Preventive screenings fr breast cancer, cervical cancer, and clrectal cancer were well belw HEDIS Natinal Guidelines. The suggested standards fr HEDIS Natinal Guidelines are as fllws: Breast Cancer Screening: 80% in the 95th percentile and 69% in the 25th percentile Cervical Cancer Screening: 82% in the 95th percentile and 73% in the 25th percentile Clrectal Cancer Screening: 68% in the 95th percentile and 50% in the 25th percentile Actual screening rates fr the ASE ppulatin were as fllws in 2014: Breast Cancer Screening 44.2% Cervical Cancer Screening 33.9% Clrectal Cancer Screening 15.8% Actual screening rates fr the PSE ppulatin were as fllws in 2014: Breast Cancer Screening 46.1% Cervical Cancer Screening 36.6% Clrectal Cancer Screening 14.5% Recmmended Slutin: Increase the awareness f age/gender-specific preventive screenings within the ppulatin. Educatin in cmbinatin with varius incentives wuld increase the ppulatin s cmpliance with preventive screenings. Increased cmpliance t preventive screenings wuld identify diseases in the early stage, thus imprving treatment utcmes and decreasing future expenditures. Establish at least five HEDIS (Healthcare Effectiveness and Infrmatin Set) gals fr the ppulatin. HEDIS is ne f the mst widely recgnized healthcare perfrmance measures in the United States. Suggested gals are as fllws: Gal 1: Increase the number f individuals between the ages f 18 t 75 wh have a diagnsis f diabetes and are cmpliant with the fllwing evidence-based medicine guidelines: Hemglbin A1c (HbA1c) testing HbA1c pr cntrl (>9.0%) HbA1c cntrl (<8.0%) HbA1c cntrl (<7.0%) fr a selected ppulatin Eye exam (retinal) perfrmed LDL-C screening LDL-C cntrl (<100 mg/dl) Medical attentin fr nephrpathy BP cntrl (<130/80 mm Hg) 2015 Human Factr Analytics, Inc. Executive Summary Page 7
Gal 2: Increase the number f individuals between the ages f 18 t 74 wh had an utpatient visit and had their bdy mass index (BMI) dcumented Gal 3: Increase the percentage f wmen between the ages f 40 t 69 wh had a mammgram t screen fr breast cancer Gal 4: Increase the percentage f wmen between the ages f 21 t 64 wh received ne r mre Pap tests t screen fr cervical cancer Gal 5: Increase the percentage f individuals between the ages f 50 t 75 wh had an apprpriate screening fr clrectal cancer Key Finding 4: Musculskeletal Diagnses Pages 22-23 and 43-44 f Ppulatin Health Management Reprt Key Finding: Expenditures fr musculskeletal-related diagnses were the secnd mst expensive diagnstic categry fr bth the ASE and PSE ppulatins in 2014 (i.e., apprximately $19.1 millin fr ASE and apprximately $22.6 millin fr PSE). An analysis was cmpleted t investigate which Musculskeletal & Cnnective Tissue claims culd ptentially be wrk-related. Wrk-related musculskeletal claims are usually assciated with jbs r crafts that require manual material handling, frequent bending and twisting, static wrk psture, r whle bdy vibratin. The results f this analysis were as fllws fr the ASE ppulatin in 2014: Back $491,575 Upper Extremity $175,948 Hand & Wrist $79,805 The results f this analysis were as fllws fr the PSE ppulatin in 2014: Back $585,844 Upper Extremity $229,826 Hand & Wrist $112,791 Recmmended Slutin: Based n the high frequency and csts assciated with musculskeletal medical claims, cnsider the implementatin f pre-emplyment physical ability testing that simulates the essential functins f a particular jb r craft. Cnduct a jb task analysis identify the essential functins f high-risk jbs. EEOC has specific guidelines fr the design and implementatin f physical ability tests. A well-designed physical ability test can help prevent wrksite injury. Key Finding 5: Medicatin Cmpliance Pages 25, 27, and 51-52 f Ppulatin Health Management Reprt Key Finding: Calculatin f a Medicatin Pssessin Rati revealed that within the ASE ppulatin in 2014, 19,605 individuals were prescribed hypertensin medicatin (97.5% MPR) and 6,463 were prescribed statin medicatin (i.e., lipid management drugs) (98.1% MPR). Within the ASE ppulatin, there were 17,308 unique individuals in 2014 wh had a diagnsis f hypertensin and 9,637 wh had a diagnsis f hyperlipidemia. Fr the PSE ppulatin in 2014, 13,543 individuals were prescribed hypertensin medicatin (96.6% MPR) and 4,060 were prescribed statin drugs (98.5% MPR). Within the PSE ppulatin, there were 18,575 unique individuals in 2014 wh had a diagnsis f hypertensin and 10,164 wh had a diagnsis f hyperlipidemia. 2015 Human Factr Analytics, Inc. Executive Summary Page 8
The Medicatin Pssessin Rati determines an individual s cmpliance t medicatins. Hwever, it nly takes int accunt individuals wh have been prescribed medicatin and have refilled the prescriptin at least nce. It des nt take int accunt the ther peple wh may have a diagnsis, but n prescriptin has been tracked. Fr example, a persn may have a diagnsis fr hypertensin, but they may nt appear in the pharmacy data due t the fact that they either have n prescriptin r they have failed t fill a prescriptin they were prescribed. Recmmended Slutin: Implement a slutin that identifies all individuals wh are nncmpliant with medicatins and implement a mail-ut reminder t the member s hme address. Cmbine this strategy with an incentive cnnected t the member s benefit plan design. Key Finding 6: Patient/Physician Cmmunicatin Pages 22-23 f Ppulatin Health Management Reprt Key Finding: It shuld be nted that high frequencies f Symptms, Signs, and Ill-Defined Cnditins (i.e,. the furth mst expensive diagnstic categry fr bth the ASE and PSE ppulatins in 2014) culd be a strng predictr f pr patient/physician cmmunicatin. Within this categry, n specific diagnsis is rendered, yet treatment cst is experienced. Fr example, with a diagnsis f Symptms, Signs, and Ill-Defined Cnditins invlving the abdmen, in reality the diagnsis culd be mre specific as Gastr Esphageal Reflux Disease (GERD). Recmmended Slutin: Persnal electrnic health recrds can help imprve the accuracy f an individual s diagnsis, and writing dwn all symptms prir t a physician visit can als imprve the accuracy f diagnsis. Key Finding 7: Avidable Emergency Rm Visits Pages 63-64 f Ppulatin Health Management Reprt Key Finding: Avidable Emergency Rm visits fr the ASE and PSE ppulatins cmbined amunted t greater than $1.5 millin in excess spending (Refer t Attachment 5). Avidable ER visits are defined are as thse visits which culd have been apprpriately treated in anther setting at the time the visit ccurred. The State f Washingtn, thrugh sampling f 53 hspitals and 2.2 millin patients, established the definitin f avidable ER visits. Avidable ER visits have the fllwing statistics: 1 ut f 9 visits is avidable. Avidable visits accunt fr apprximately 11 percent f the verall ER spend. Children that are less than 18 years f age cmprise 1/3 f all avidable visits. The majrity f avidable visits are cmprised f females. The uninsured have apprximately the same rate f avidable visits as cmpared t the insured. The majrity f avidable ER visits ccur between 12 p.m. and 8 p.m. Recmmended Slutin: In rder t effectively reduce avidable ER visits, frequent flyers need t be identified and cnnected with a primary care physician. The State f Washingtn research indicated that if these individuals are assigned a primary care physician, avidable ER visits will be reduced by apprximately 58 percent. It wuld als be suggested t distribute medical selfcare guides t help peple differentiate between an emergency and a situatin that can be reslved at an alternative setting. One ther leading cause fr avidable ER visits is related t drug seeking behavir; this can be limited by urging hspitals t limit the amunt f pain management drugs that are prescribed, especially piid-based medicatins. 2015 Human Factr Analytics, Inc. Executive Summary Page 9
Key Finding 8: Warehuse Data in Relatinal Database Key Finding: It is recmmended that State f Arkansas cnsider warehusing all relevant healthcare data within a relatinal database that has the ability t query the data. By having the ability t query and explre archival and current healthcare data, empirical evidence can be gained that will supprt strategic risk management decisin-making. Additinally, such data analysis can serve as a vital tl t measure the pre/pst effectiveness f varius ppulatin health strategies and interventins. Summary The verall gal f this ppulatin health analysis is t bring meaningful use t the 2013-2014 medical and pharmacy data fr the ASE and PSE ppulatins. Meaningful use is defined as gaining insight int future ppulatin health management strategies that will prmte the health and well-being f the ASE and PSE ppulatins f the State f Arkansas. This analysis will prvide a baseline t measure future success f ppulatin health management strategies (e.g., wellness, pharmacy management, disease management, and adherence t evidence-based medicine guidelines). 2015 Human Factr Analytics, Inc. Executive Summary Page 10