• Center on Health Equity and Access
  • Clinical
  • Health Care Cost
  • Health Care Delivery
  • Insurance
  • Policy
  • Technology
  • Value-Based Care

Enhanced Risk Prediction Model for Emergency Department Use and Hospitalizations in Patients in a Primary Care Medical Home

The American Journal of Managed CareJuly 2016
Volume 22
Issue 7

An enhanced risk model incorporating medication use, prior healthcare utilization, and mental health with comorbid health conditions predicts healthcare utilization better than health conditions alone.


Objectives: With the advent of healthcare payment reform, identifying high-risk populations has become more important to providers. Existing risk-prediction models often focus on chronic conditions. This study sought to better understand other factors to improve identification of the highest risk population.

Study Design: A retrospective cohort study of a paneled primary care population utilizing 2010 data to calibrate a risk prediction model of hospital and emergency department (ED) use in 2011.

Methods: Data were randomly split into development and validation data sets. We compared the enhanced model containing the additional risk predictors with the Minnesota medical tiering model. The study was conducted in the primary care practice of an integrated delivery system at an academic medical center in Rochester, Minnesota. The study focus was primary care medical home patients in 2010 and 2011 (n = 84,752), with the primary outcome of subsequent hospitalization or ED visit. A total of 42,384 individuals derived the enhanced risk-prediction model and 42,368 individuals validated the model. Predictors included Adjusted Clinical Groups—based Minnesota medical tiering, patient demographics, insurance status, and prior year healthcare utilization. Additional variables included specific mental and medical conditions, use of high-risk medications, and body mass index.

Results: The area under the curve in the enhanced model was 0.705 (95% CI, 0.698-0.712) compared with 0.662 (95% CI, 0.656-0.669) in the Minnesota medical tiering-only model. New high-risk patients in the enhanced model were more likely to have lack of health insurance, presence of Medicaid, diagnosed depression, and prior ED utilization.

Conclusions: An enhanced model including additional healthcare-related factors improved the prediction of risk of hospitalization or ED visit.

Am J Manag Care. 2016;22(7):475-483

Take-Away Points

  • Many risk models that predict hospitalization or emergency department (ED) use are based on comorbid health burden.
  • We investigated other potential predictors, including previous utilization, and found an association of higher hospitalization and ED use with the following: Minnesota medical tiering, 2 prior ED visits, warfarin use, no insurance or Medicaid, being aged over 70 years, and obesity stage 3.
  • These findings help to develop new models for determining patients at risk for adverse health outcomes. They focus on potential future areas of clinical practice and research on social determinants of health.

Changing the payment incentives to improve the organization of care delivery, including accountable care organizations, has led to a reorganization of delivery of care focusing on patient-centered medical homes.1 Many patient-centered medical homes stratify their populations and try to align the level of care with the needs of their patients. Providers manage the most complex patients with increased resources to avoid adverse outcomes requiring high-cost care in acute settings.

Two key questions need to be answered: Who is likely to have utilization, and what can be done for these patients? These medically complex patients could be better managed through care coordination programs that are integrated within the delivery system, such as Guided Care, which incorporates specially trained nurses to supplement primary care in a comprehensive care model that provides coordinated, patient-centered primary care for patients with complex needs.2 Different methods for identifying these patients have been developed, including provider identification and referral.3 A hybrid system that utilizes provider selection and an automated risk stratification model may be ideal. Most of the currently available public and proprietary risk models and instruments to identify the population for inclusion in more intensive management programs are based on number and combination of comorbidities. These instruments include self-administered tools such as the Predicting Risk of Admission (PRA) survey, which incorporates self-rated health and comorbid conditions as well as prior healthcare utilization.4 Another model utilizing medical record data is the Elder Risk Assessment (ERA) index, which determines risk of hospitalization based upon increasing age and previous hospitalization as well as comorbid health conditions.5

A third risk model, Adjusted Clinical Groups (ACG), uses medical care claims data and stratifies patients based on age and existing chronic conditions.6 The state of Minnesota pays medical homes a care management fee based on Minnesota medical tiering. Minnesota medical tiering is a system of classifying and counting comorbidities originally based on collapsing chronic diseases from ACGs into a body system count.7 This system can also be manually applied without the proprietary software. Our clinical practice uses Minnesota medical tiering to identify appropriate patients for care management. Our prior work comparing these instruments, including Minnesota medical tiering, indicates that these instruments are similar in terms of predicting the risk of hospitalization or emergency department (ED) visit; however, the ACG performed slightly better while the Charlson Comorbidity Index performed worse.8 Although the administrative data-based risk identification methods have been validated and can predict hospitalization with C statistics of 0.67-0.73,8 other potential predictors may improve current risk prediction models and could provide information useful in developing tailored interventions to manage a patient’s condition.

Our clinical experience indicates that coordinating the management of multiple chronic conditions is important in reducing rehospitalizations among geriatric patients with multimorbidity, but other factors may also be important in predicting and managing an individual’s risk.9 In our practice, many patients predicted to be high risk are excluded from care management interventions because they have concurrent mental health or social conditions that care coordinators are unprepared to address. Body mass index (BMI), medication use,10 living environment, English as a second language, previous healthcare use including specialty visits, insurance, and mental health status11 might all be important factors. Patients with those issues may need a different approach, including more support from social services, pharmacy, and psychology.

Obesity is known to influence utilization, but it is widely underdiagnosed and so may be missed in many comorbidity counts.12 The use of anticoagulation therapy, pain, and diabetic medications can lead to an increased need for unplanned acute care and may be amenable to pharmacy consultation.13 Patients living on their own or in dysfunctional families may not be able to cope with increased treatment burdens and may benefit from social services.14,15 Similarly, those who need language interpretation services may need extra assistance. Patients with a high number of specialty visits may have uncoordinated care and may need help in coordinating their care. Lack of insurance and lower socioeconomic status are associated with increased rates of hospitalization.16 Patients experiencing mental health issues such as depression can also have higher readmission rates.7,17 Providers and systems with established comorbid health models should understand the potential roles of these additional predictors for hospitalization.

It is unclear how well these additional factors would predict hospitalization when added to an already established clinical model based on administrative data. To answer this question, we performed a retrospective cohort study comparing a standard risk stratification model of Minnesota medical tiering with an enhanced model containing additional predictors of demographics, selected use of medications, mental health conditions, other specific chronic medical conditions, prior healthcare utilization, and insurance status. We sought to understand how well the enhanced model predicts hospitalization and ED use in a subsequent year.



We constructed a retrospective cohort study to develop and validate a clinical prediction model for hospitalization. The study was approved by the Mayo Clinic Institutional Review Board.


The study was conducted at an academic medical center in Rochester, Minnesota. The population came from the primary care medical practice, Employee and Community Health (ECH), which includes 4 primary care clinics in Dodge and Olmsted Counties, Minnesota. Primary care is provided by family medicine, primary care internal medicine, and community pediatric and adolescent medicine.

Patient Population

Patients 18 years or older who were assigned to a primary care provider within any of the primary care clinics for all 12 months in 2010 (base year) and throughout 12 months or until death in 2011 (assessment year) were included in the analysis. Subjects were excluded if they refused consent for medical record review in accordance with Minnesota state law. Of the 90,411 patients within the ECH program, as of December 31, 2010, 84,752 (93.7%) gave consent for medical record review.

Data Collection

All information for enrolled ECH patients was electronically abstracted from the electronic medical record (EMR) and administrative databases for 2010 and 2011.

Outcomes and Predictors

We modeled a combined binary outcome of hospitalization and/or ED visit (hereafter: hosp/ED) at St. Mary’s Hospital or Rochester Methodist Hospital in the assessment year. Hospitalization was defined as any elective or emergent overnight. To avoid double counting, ED visits exclude those resulting in a hospitalization.

Demographic variables—age, gender, marital status, and socioeconomic status—were collected at the end of the base year. Age was grouped into 6 categories: 18 to 29 years, 30 to 39 years, 40 to 49 years, 50 to 59 years, 60 to 69 years, and 70 years or older. Socioeconomic status was determined by insurance type (Medicaid or no insurance versus other insurance type).

As Minnesota medical tiering is the basis for care coordination payments for patient-centered medical homes in Minnesota, we used it as the basis for our risk model development. Medical conditions are based on diagnosis codes billed by clinical providers.18 Minnesota medical tiering has 5 categories from 0 with no conditions to tier 4 with more than 10 conditions. In our care management program, high-risk patients possess Minnesota medical-tiering scores of 3 or 4.

Candidate predictors for identifying high-risk patients included primary speaking language not being English; high-risk medications (warfarin, insulin, narcotics); and previous healthcare utilization in specific settings (ED visits, hospitalization, specialty provider visits). Medications were based on the presence of outpatient pharmacy orders in 2010 written by any healthcare providers within the system. Medication use was determined from the EMR, not from filled prescriptions. The medications included warfarin, narcotics and insulin. Non—English speaking status was assigned to those patients needing an interpreter, which was documented within the electronic health record. We included all diagnosis codes (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM]) from hospitalizations, ED visits, and primary and specialty care visits to identify comorbid mental health and medical conditions, based on a published method.19 Each outpatient visit had 4 potential ICD-9-CM codes and inpatient visits had 25 ICD-9-CM codes.

BMI—recorded as weight (kilograms) / height (meters)2—was also included as a potential predictor. BMI was assessed as being the most recent value in the record within 2 years prior to January 1, 2011. Specific BMI categories followed standard World Health Organization criteria: underweight (BMI <18.5); normal weight (BMI 18.5-24.9); overweight (BMI 25-29.9); stage 1 obesity (BMI 30-34.9); stage 2 obesity (BMI 35-39.9); and stage 3 obesity (BMI >40).20

Statistical Analysis and Model Building

We developed the enhanced model to identify those patients at high risk of hosp/ED within the next year. The 2 models compared in the analysis consisted of Minnesota medical tiering and the enhanced model, which incorporated additional candidate factors. Data were randomly divided into development (50%) and validation cohorts.

We derived the final enhanced model from a stepwise logistic regression followed by a sensitivity analysis seeking stability in the predictors determined by the severity of the outcome variable. Predictors in the saturated model consisted of 72 main factors as well as all possible 1-way and 2-way interaction terms. We determined stability of the variables by changing the number of ED visits or inpatient (IP) visits in the outcome variable. We performed the saturated stepwise model while incrementally increasing the number of ED visits or IP visits. The final set of variables for each change in outcome variable was captured. We then considered a count of how many times a variable had a P value ≤.05 in the model. Finally, we included any variable that remained significant in 4 or more of the 11 models considered.

The stepwise regression was repeated 100 times and each repetition considered the variables in random order to determine if order affected the results of the stepwise algorithm. Each iteration produced the same model. Effects were entered into and removed from the model so that each forward selection step could be followed by a backward elimination step. The algorithm stopped when no further effects could be added to the model or if the current model was identical to a previous model.

Comparisons between prediction models focused on predictability and precision. We used C statistics (area under the receiver operating characteristic [ROC] curve [AUC]) as our measure of predictability. For assessment of precision, we compared the sets of high-risk patients found within the tier 3 and 4 groups in Minnesota medical tiering with an equal number of high-scoring patients from the enhanced model. Statistical analysis was performed using SAS version 9.3 (SAS Institute, Cary, North Carolina).


Patient Characteristics

Table 1

A total of 42,384 and 42,368 patients were included in the development and validation samples, respectively. The development and validation data sets were similar. There were no differences in sex, age decile, BMI, marital status, insurance status, prior healthcare utilization, comorbid health status, or Minnesota medical tiering (). In addition, the 2 samples had similar rates of hospitalization and ED visits with 18.2% of the development sample and 17.6% of the validation sample having a hospitalization or ED visit in the subsequent year.

Initial Univariable Model

Table 2

Those patients who belonged to the highest category of Minnesota medical tiering (4) had the greatest odds of incurring any hospitalization or ED visit with an odds ratio of 18.2 (95% CI, 15.6-21.24) compared with the lowest tier category (0). Next most important was prior utilization of 2 or more ED visits with an odds ratio of 7.13 (95% CI, 6.34-8.01) for predicting hosp/ED. Similarly, 2 or more previous hospital stays had an odds ratio of 4.28 (95% CI, 3.90-4.69) for predicting hosp/ED. Mental health conditions, including substance abuse, personality disorder or dementia, each had odds near 3 for predicting hosp/ED. Specifically, depression had an odds ratio of 2.92 (95% CI, 2.71-3.15). Among other medical conditions, cardiac disease with heart failure and pulmonary heart disease had odds ratios over 5. All specific conditions with odds ratios over 2 are reported in Table 1. All specific conditions are listed in . Patients in each of the medication categories had odds ratios greater than 2, as did those utilizing 3 or more specialist visits in the prior year. Patients with a language interpreter did not have elevated risk of hosp/ED (Table 2).

Multivariable Model

Table 3

The multivariable enhanced tiering model is presented in . The final model included 30 main effects (representing 16 factors) and 4 2-way interaction terms (more detailed results are available from the authors).


Of notable mention, being in the highest medical tier remains the strongest predictor for hosp/ED after the addition of other factors with an odds ratio of 3.50 (95% CI, 2.78-4.42). More than 1 previous ED visit also resulted in an increased odds ratio of 3.17 (95% CI, 2.78-3.60). Several significant interaction terms are noted in Table 3. In the validation cohort, the C statistic for the enhanced multivariable model was 0.711 (95% CI, 0.704-0.717) compared with a C statistic for the Minnesota medical tiering alone model of 0.667 (95% CI 0.660-0.673). These C statistics were actually slightly higher in the validation cohort than in the development cohort (enhanced model: 0.705 [95% CI, 0.698-0.712]; Minnesota medical tiering: 0.662 [95% CI, 0.656-0.669]). The differences between the ability of the enhanced model to predict hospitalization and ED use and Minnesota medical tiering are displayed in the ROC curves found in the .

Table 4

Many different patients were identified as being high-risk in the enhanced model as compared with those in the original Minnesota medical tiering model (tier 3 or 4). presents the characteristics of high-risk patients identified with both models as well as those unique to the 2 approaches. We found that patients identified as high-risk in the enhanced model were much more likely to experience hospital utilization in 2011 than those in the Minnesota medical tiering-only model (P <.001). Patients identified as high-risk based only on the enhanced model, compared with those identified only through Minnesota medical tiering, were of younger age, had higher previous healthcare utilization, and had more frequent mental health conditions. Many of the high-risk enhanced model patients were aged less than 40 years compared with only 11% of patients being aged less than 40 years in the Minnesota medical tiering model.


In this retrospective cohort of patients receiving care within the ECH, several variables were important in predicting hosp/ED in addition to the number of chronic comorbidities currently used in Minnesota medical tiering and our care coordination identification. We found that age, BMI, previous healthcare utilization, mental health conditions, anemia, heart failure, epilepsy, hyperlipidemia, warfarin use, and narcotic use all simultaneously contributed to the prediction of hospitalization and ED visits in the next year. This enhanced model had an AUC of 0.711 (95% CI, 0.704-0.717) for predicting hosp/ED, as compared with 0.667 (95% CI, 0.660-0.673) found in the model utilizing only Minnesota medical tiering. Previous work comparing different risk stratification methods for hospitalization determined slight superiority of ACG compared with the ERA, Hierarchical Condition Categories, Minnesota medical tiering, and chronic care counting instruments.8 The enhanced model takes into consideration previous hospitalization, specific high-risk illnesses, mental health conditions, and high-risk medication use (eg, warfarin, narcotics) that are not universally accounted for in other models. In comparing high-risk groups of the same size from both models and excluding patients identified in both models, 47% of the patients in the enhanced model experienced hosp/ED compared with 32.5% of patients from the Minnesota medical tiering model. The enhanced model accounts for factors in a younger population (eg, mental health and previous utilization) that are not detected in the Minnesota medical tiering model.

Previous hospital and ED utilization served as strong predictors for hosp/ED in the following year. Specifically, 2 or more prior ED visits presented an odds ratio (OR) close to 3 while 1 prior hospital stay resulted in an OR of 1.79 (95% CI, 1.66-1.93). Together, hospitalization and ED visits often indicate a medically complex individual or an individual with severe illness. Many risk stratification models have utilized previous healthcare usage as a predictor for future healthcare use. The PRA incorporates previous hospitalization within the past year as a factor predictive of future hospitalization.4 Similarly, the ERA also includes previous hospital days to calculate a risk score for future hospitalization.5 Previous ED visits as well as previous hospitalization have been associated with increased hospital admission in a multi-centered VA study of 1378 patients designed to identify factors of hospitalization.21

Concurrent mental health conditions also proved indicative of potential hospitalization and utilization. In the multivariable analysis, patients who had depression and saw a specialist had an association with increased hosp/ED. Patients with substance abuse had higher risk of hosp/ED. Previous studies have also shown increased risk of hospitalization with concurrent mental health disorders. In a study of 771 patients with dementia, 48% of patients had a hospital admission during the study.22 In 8812 hypertensive patients, mood disorders and personality disorders resulted in higher ORs of hospitalizations of 1.3 and 1.2, respectively.23 The identification of mood disorders as a potential risk for hospitalization or ED visits can help providers initiate specific care plans. In particular, the Depression Improvement Across Minnesota, Offering a New Direction care management model has been very successful in assisting depressed patients, with 6-month depression remission rates at 27% compared with 0% to 11% in controls.24

Medication use continues to serve as an important predictor of hospitalization. We found that warfarin use had more than a 2-fold increase in hosp/ED compared with those patients not on warfarin. We also found narcotics in the final model as a significant factor. These medications underscore the potential importance of anticoagulation and pain medications in predicting hospitalization. In a study of 5077 cases of hospitalization following an ED visit, investigators found that 67.0% (95% CI, 60.0-74.1) of hospitalizations directly resulted from 4 drug classes (warfarin, anti-platelet agents, insulin, and oral diabetic agents).25 Many risk stratification models, including Minnesota medical tiering, PRA,4 and ERA5 do not incorporate use of specific medications. The clinical utility of our findings indicate possible expansion of medication therapy management, which is an important part of the Medicare part D program.26

Other factors included in the final model possess biological plausibility. Many patients did not have electronically retrievable BMI assessments; however, those patients were significantly less likely to have hosp/ED. Patients with obese BMI experienced significantly increased risk of hosp/ED. In a study of 20,985 type 1 diabetics, obesity increased hospitalization rates with a hazard ratio of 2.90 (95% CI, 1.92-4.37) for BMI ≥35 kg/m2 compared with normal BMI.27 Lack of insurance or presence of Medicaid coverage placed the patient at risk of hosp/ED. A recent meta-analysis indicated that having no insurance and low socioeconomic status increased readmission rates.28 Improving access to care as well as providing community support may be important practice measures in treating patients with Medicaid or no insurance.


There are inherent weaknesses and challenges to this study. For example, it is possible that we missed some outcomes because some patients may seek care outside of the academic healthcare medical system. The predictor variables—demographics, comorbidities, medications, prior utilization, and BMI&mdash;should be readily obtainable from clinical and administrative sources. However, generalizability of our model could be limited in that not every healthcare system may have all variables readily available. There are some potential biases with self-report and potential for miscoded or missing data. Despite these limitations, the utility of administrative indexes to identify potentially high-risk individuals is very high. The clinical applications of previous administrative indexes have been seen within our institution.5 Lastly, the generalizability of our findings may be limited as Olmsted County has a largely white and educated population.29


The enhanced model identifies a large subset of high-risk individuals not easily identified by comorbid health burden alone. Some factors significant in the prediction model can be modified and other factors can possibly be addressed with specific actions. Future work will focus on identifying high-risk individuals with characteristics that may be modified by aspects of care coordination models. In particular, further studies or clinical models could include medication management, care transitions after hospitalization, and new models of co-management of mental health conditions. Mental health professionals and pharmacists could have expanded roles on care management teams. The potential to individualize the care model for high-risk patients increases with the enhanced model.

Author Affiliations: Division of Primary Care Internal Medicine, Department of Medicine (PYT), and Department of Health Science Research (HCH, LRS, NDS, JMN), Mayo Clinic, Rochester, MN.

Source of Funding: We would like to acknowledge the Mayo Clinic Department of Medicine for providing funding support of this project. We would like to acknowledge the Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery of Mayo Clinic for funding support.

Author Disclosures: Dr Takahashi is on the Medical Advisory Board of Axial Exchange. Dr Shah has previously received a grant from CMS to improve delivery of care; no funding from that grant was used in this work, but work on the grant influenced our approach and literature review. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (HCH, LRS, JMN, NDS, PYT); acquisition of data (LRS, JMN, NDS, PYT); analysis and interpretation of data (HCH, LRS, JMN, PYT); drafting of the manuscript (HCH, LRS, JMN, PYT); critical revision of the manuscript for important intellectual content (JMN, NDS, PYT); statistical analysis (HCH, JMN); provision of patients or study materials (JMN); obtaining funding (JMN, PYT); administrative, technical, or logistic support (JMN); and supervision (JMN, PYT).

Address correspondence to: James M. Naessens, ScD, Mayo Clinic, 200 First St SW, Rochester, MN 55905. E-mail: naessens@mayo.edu.


1. Dove JT, Weaver WD, Lewin J. Health care delivery system reform: accountable care organizations. J Am Coll Cardiol. 2009;54(11):985-988.

2. Boult C, Reider L, Leff B, et al. The effect of guided care teams on the use of health services: results from a cluster-randomized controlled trial. Arch Intern Med. 2011;171(5):460-466.

3. Freund T, Peters-Klimm F, Rochon J, et al. Primary care practice-based care management for chronically ill patients (PraCMan): study protocol for a cluster randomized controlled trial [ISRCTN56104508]. Trials. 2011;12:163.

4. Boult C, Dowd B, McCaffrey D, Boult L, Hernandez R, Krulewitch H. Screening elders for risk of hospital admission. J Am Geriatr Soc. 1993;41(8):811-817.

5. Crane SJ, Tung EE, Hanson GJ, Cha S, Chaudhry R, Takahashi PY. Use of an electronic administrative database to identify older community dwelling adults at high-risk for hospitalization or emergency department visits: the elders risk assessment index. BMC Health Serv Res. 2010;10:338.

6. Weiner JP, Starfield BH, Steinwachs DM, Mumford LM. Development and application of a population-oriented measure of ambulatory care case-mix. Med Care. 1991;29(5):452-472.

7. Chaudhry SA, Tariq N, Majidi S, et al. Rates and factors associated with admission in patients presenting to the ED with TIA in the United States-2006 to 2008. Am J Emerg Med. 2013;31(3):516-519.

8. Haas LR, Takahashi PY, Shah ND, et al. Risk-stratification methods for identifying patients for care coordination. Am J Manag Care. 2013;19(9):725-732.

9. Takahashi PY, Naessens JM, Peterson SM, et al. Short-term and long-term effectiveness of a post-hospital care transitions program in an older, medically complex population. Healthc (Amst). 2016;4(1):30-35. doi: 10.1016/j.hjdsi.2015.06.006.

10. Tarride JE, Haq M, Taylor VH, et al. Health status, hospitalizations, day procedures, and physician costs associated with body mass index (BMI) levels in Ontario, Canada. Clinicoecon Outcomes Res. 2012;4:21-30.

11. Wagner JA, Pietrzak RH, Petry NM. Psychiatric disorders are associated with hospital care utilization in persons with hypertension: results from the National Epidemiologic Survey on alcohol and related conditions. Soc Psychiatry Psychiatr Epidemiol. 2008;43(11):878-888.

12. Moriarty JP, Branda ME, Olsen KD, et al. The effects of incremental costs of smoking and obesity on health care costs among adults: a 7-year longitudinal study. J Occup Environ Med. 2012;54(3):286-291. doi: 10.1097/JOM.0b013e318246f1f4.

13. Budnitz DS, Lovegrove MC, Crosby AE. Emergency department visits for overdoses of acetaminophen-containing products. Am J Prev Med. 2011;40(6):585-592.

14. Vermeire E, Hearnshaw H, Van Royen P, Denekens J. Patient adherence to treatment: three decades of research. a comprehensive review. J Clin Pharm Ther. 2001;26(5):331-342.

15. Vijan S, Hayward RA, Ronis DL, Hofer TP. Brief report: the burden of diabetes therapy: implications for the design of effective patient-centered treatment regimens. J Gen Intern Med. 2005;20(5):479-482.

16. Bula CJ, Wietlisbach V, Burnand B, Yersin B. Depressive symptoms as a predictor of 6-month outcomes and services utilization in elderly medical inpatients. Arch Intern Med. 2001;161(21):2609-2615.

17. Reese RL, Freedland KE, Steinmeyer BC, Rich MW, Rackley JW, Carney RM. Depression and rehospitalization following acute myocardial infarction. Circ Cardiovasc Qual Outcomes. 2011;4(6):626-633.

18. Care Coordination Tier Assignment Tool [version 1.0]: health care home initiative. Minnesota Department of Health website. http://www.health.state.mn.us/healthreform/homes/payment/training/complexteirtool.pdf. Accessed August 27, 2014.

19. Naessens JM, Stroebel RJ, Finnie DM, et al. Effect of multiple chronic conditions among working-age adults. Am J Manag Care. 2011;17(2):118-122.

20. BMI classification. World Health Organization website. http://apps.who.int/bmi/index.jsp?introPage=intro_3.html. Accessed August 27, 2014.

21. Smith DM, Giobbie-Hurder A, Weinberger M, et al. Predicting non-elective hospital readmissions: a multi-site study. Department of Veterans Affairs Cooperative Study Group on Primary Care and Readmissions. J Clin Epidemiol. 2000;53(11):1113-1118.

22. Fong TG, Jones RN, Marcantonio ER, et al. Adverse outcomes after hospitalization and delirium in persons with Alzheimer disease. Ann Intern Med. 2012;156(12):848-856, W296.

23. Wagner JA, Pietrzak RH, Petry NM. Psychiatric disorders are associated with hospital care utilization in persons with hypertension: results from the National Epidemiologic Survey on alcohol and related conditions. Soc Psychiatry Psychiatr Epidemiol. 2008;43(11):878-888.

24. Williams MD, Jaeckels N, Rummans TA, Somers K, Nesse RE, Gorman RS. Creating value in depression management. Qual Prim Care. 2010;18(5):327-333.

25. Budnitz DS, Lovegrove MC, Shehab N, Richards CL. Emergency hospitalizations for adverse drug events in older Americans. N Engl J Med. 2011;365(21):2002-2012.

26. Smith SR, Clancy CM. Medication therapy management programs: forming a new cornerstone for quality and safety in Medicare. Am J Med Qual. 2006;21(4):276-279.

27. Vestberg D, Rosengren A, Olsson M, Gudbjornsdottir S, Svensson AM, Lind M. Relationship between overweight and obesity with hospitalization for heart failure in 20,985 patients with type 1 diabetes: a population-based study from the Swedish National Diabetes Registry. Diabetes Care. 2013;36(9):2857-2861.

28. Calvillo-King L, Arnold D, Eubank KJ, et al. Impact of social factors on risk of readmission or mortality in pneumonia and heart failure: systematic review. J Gen Intern Med. 2013;28(2):269-282.

29. St Sauver JL, Grossardt BR, Leibson CL, Yawn BP, Melton LJ 3rd, Rocca WA. Generalizability of epidemiological findings and public health decisions: an illustration from the Rochester Epidemiology Project. Mayo Clin Proc. 2012;87(2):151-160.&ensp;

Related Videos
© 2024 MJH Life Sciences
All rights reserved.