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Healthcare 2018, 6(4), 138; https://doi.org/10.3390/healthcare6040138

Project Report
Who Has an Unsuccessful Observation Care Stay?
1
Department of Emergency Medicine, University of California, Los Angeles, CA 90024, USA
2
Department of Internal Medicine, Ohio State University, Columbus, OH 43210, USA
3
Divisions of General Internal Medicine and Health Services Research, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
4
Department of Medicine, Greater Los Angeles Veterans Affairs Healthcare System, Geriatric Research Education and Clinical Center (GRECC), Los Angeles, CA 90073, USA
5
Department of Medicine, University of California, Los Angeles, CA 90024, USA
*
Author to whom correspondence should be addressed.
Received: 23 August 2018 / Accepted: 19 November 2018 / Published: 27 November 2018

Abstract

:
Background: With the recent increase use of observation care, it is important to understand the characteristics of patients that utilize this care and either have a prolonged observation care stay or require admission. Methods: We a conducted a retrospective cohort study utilizing 5% sample data from Medicare patients age ≥65 years that was nationally representative in the year 2013. We performed a generalized estimating equation (GEE) logistic regression analysis to evaluate the relationship between an unsuccessful observation stay (defined as either requiring an inpatient admission from observation or having a prolonged observation stay) compared to having successful observation care. Observation cut offs of “successful” vs. “unsuccessful” were based on the CMS 2 midnight rule. Results: Of 154,756 observation stays in 2013, 19 percent (n = 29,604) were admitted to the inpatient service and 34,275 (22.2%) had a prolonged observation stay. The two diagnoses most likely to have an unsuccessful observation stay were intestinal infections (OR 1.56, 95% CI 1.32–1.83) and pneumonia (OR 1.26, 95% CI 1.13–1.41). Conclusion: We found patients placed in observation care with intestinal infections and pneumonia to have the highest odds of either being admitted from observation or having a prolonged observation stay.
Keywords:
observation care; outcomes; unsuccessful observation care; observation failure

1. Introduction

In recent years, there has been greater use of observation services for patients by all types of providers [1,2,3] This care provides a short-term (24–72 h) treatment and assessment, is billed as an outpatient visit, and can take place in the emergency department, inpatient units, special observation units, or any other monitored settings [4] It is utilized by providers to “observe” patients in a monitored setting, usually a hospital. Patients placed in observation care are not well enough to be discharged home and not sick enough to require a prolonged admission. Due to the nature of observation care, patients placed in this care are not expected to require prolonged monitored care.
While the idea of observing a patient dates back to Hippocrates, the increased use of observation care in the US is relatively new [5]. As providers better understand the roles and uses of observation care stays, they require an improved understanding of the outcomes of patients placed in observation care. For inpatient providers and hospital administrators, patients who have unsuccessful observation stays either require an inpatient admission or to have a prolonged observation stay. It is important for both providers and administrators to understand the characteristics of these patients as unsuccessful observation stays are costly to the system, not clinically expected, and may result in unnecessary care. Currently, there are no known studies that assess the characteristics of patients who have an unsuccessful observation stay.
We evaluated 154,756 patients with Medicare Insurance age ≥65 years placed in any US hospital observation care in 2013. The objective of the study was to evaluate the characteristics of patients who utilize observation care and subsequently have an unsuccessful stay, either by being admitted to the inpatient service or by having a prolonged observation stay, defined as ≥2 midnights.

2. Methods

2.1. Study Design

We performed a retrospective cohort study of a 5% sample of Medicare patients that was nationally representative. All patients were placed in observation care in 2013. The IRB at the University of California, Los Angeles approved the study.

2.2. Setting and Selection of Participants

Participants in the study were age ≥65 years at the time of their first day of observation care use. If participants had multiple observation care stays, then only the first stay of the year was included in the analytic sample. Patients who had an observation stay more than 30 days or who were deceased during the observation stay were excluded.

2.3. Data Sources

Visit records used for the study analysis were obtained from the Center for Medicare and Medicaid (CMS) Outpatient File, the CMS Inpatient MEDPAR (Medicare Provider Analysis and Review) file, the Master Beneficiary File, and the Chronic Conditions file for 2013.

2.4. Measures

Patient comorbidities were derived through the CMS Chronic Conditions file which was linked to the visit records using Claim ID. The CMS Chronic Conditions file contains information regarding the sum total of chronic conditions prior to the observation stay (0–27). Medical diagnoses were obtained based on an algorithm developed by the PI of the study [6,7,8]. In brief, a cross-walk mapping process was linked to the primary ICD-9 code for each observation stay through use of the Multi-level Clinical Classification system (CCS) codes provided by the Healthcare Cost and Utilization Project (HCUP) [9]. The PI developed a total of 39 categories, which have been outlined in the Appendix A. Having used the emergency department (ED) immediately prior to the observation stay, inpatient admission, and use of a skilled nursing facility (SNF) were determined based on Revenue Center Codes as well as charges made to Medicare.
Observation care cut-offs (successful vs. unsuccessful) were based on the CMS 2 midnight rule billing criteria [10] as well as discussion with a set of hospital administrators and inpatient physicians at UCLA and other hospitals. The terms “successful” vs. “unsuccessful” were also obtained through discussion with the administrators and providers. “Unsuccessful” was defined as having at least 2 midnight observation stays or being transferred to the inpatient service. Observation stays of 0 days required at least an 8 h placement in observation care to be billed as “observation”. Each day of observation care usage (i.e., 1 or 30) required the same number of midnights as days.

2.5. Data Analysis

Patient characteristics (demographic and clinical) as well as the diagnoses were summarized for the two clinical outcomes following an observation stay (successful observation care stay and unsuccessful observation care stay). In addition, both descriptive statistics and frequency distributions for continuous and categorical variables were generated.
Candidate factors included demographic characteristics, patient comorbidities proxied by the number of CMS chronic conditions, and observation care diagnoses. Clinical Outcomes were modeled using a Generalized Estimating Equation (GEE) logistic regression [11]. The model included all candidate factors as fixed effects and provider-level random effects that accounted for multiple observations within providers.
The model evaluated the factors associated with unsuccessful observation care (inpatient admission from observation care or observation care 2–30 days) vs. successful observation care (0 or 1 days/midnights of observation care which equates to a maximum of 47 h and 59 min). Adjusted odds ratios (AOR) and 95% confidence interval estimates were generated from this analysis. The reference groups for all analyses were the following: Age 65–69, female gender, weekday initial observation placement, observation placement from a non-ED, never used a SNF, no chronic conditions, and observation care diagnosis of “Urinary Tract Infection”. In addition, the study group conducted additional sensitivity analyses regarding patients who attended the ED and weekend vs. weekday visits.

3. Results

3.1. Sample Characteristics

Table 1 describes the characteristics of the sample. There were close to twice the number of female patients as compared to male (96,742 vs. 57,994). Fifteen percent of the cohort were placed in observation on the weekend and over half of the number of patients placed in observation came from the emergency department. Of all patients placed in observation care, a total of 64,215 (41.5%) came from the ED on a weekday and 21,500 (13.9%) came from the ED on a weekend day. Table 2 describes the diagnoses of the patients with observation stays and their outcomes. Of all diagnoses, diseases of the musculoskeletal system resulted in the highest number of patients placed in observation care (N = 17,401). This diagnosis also had the highest percent of placement (76.3%) in successful observation care. The diagnosis with the greatest number of admissions from observation was pneumonia (1077/1857, 58%). The diagnosis with the greatest percent of prolonged observation was abdominal pain (36.1%).

3.2. Main Results

Figure A1 (Appendix A) describes the creation of the study cohort. There were 154,756 with an initial observation stay in 2013. Of the cohort placed in observation, 29,604 (19.1%) were admitted to the inpatient service and 34,275 (22.2%) had a prolonged observation stay. Table 3 describes the GEE results of the model assessing the factors associated with an unsuccessful observation stay (admission or >2 days) vs. successful observation care (0–1 days). The top two diagnoses most likely to have an unsuccessful observation stay were intestinal infections (AOR 1.56, 95% CI 1.32–1.83) and pneumonia (AOR 1.26, 95% CI 1.13–1.41). Patients placed in observation care on a weekend (AOR 1.28, 95% CI 1.24–1.32), came from the emergency department (AOR 2.84, 95% CI 2.74–2.95) or utilized a skilled nursing facility (AOR 2.85, 95% CI 2.68–3.02) also had high odds of an unsuccessful observation stay.

4. Discussion

In recent years, there has been a greater use of observation care [1,2,12,13] This type of “temporary” care allows providers to place patients in a monitored setting, usually a hospital, where they can be watched for 0–48 h while being considered an outpatient encounter [5] For providers, administrators, and health policy experts, it is important to understand the type of patients that have an unsuccessful observation stay, defined as either having a prolonged observation stay or getting admitted from observation care, as having an unsuccessful observation care stay is not only unexpected to the health care system but it may result in greater cost and unnecessary care for the system. We found that patients with intestinal infections and pneumonia have the highest likelihood of having an unsuccessful observation care stay. In addition, we also found that patients coming from the ED, seen on a weekend as compared to weekday, and having been placed in a skilled nursing facility to have a higher rate of an unsuccessful observation stay.
The diagnosis with the highest odds of having an unsuccessful observation care stay was an intestinal infection, ranging from a rare diagnosis such as Cholera or Shigella to an ill-defined diagnosis. An intestinal infection is commonly a condition that is transitory in nature and while physically uncomfortable, less likely to require aggressive treatment. The findings of this study suggest that if a patient requires placement in the hospital, there may be additional factors not identifiable in administrative data that could lead to prolonged care such as dehydration and/or requirement of an extended course of treatment.
Pneumonia had the second highest odds of an unsuccessful observation care stay. Over 50% of pneumonias are classified as community acquired pneumonia [14]. While the epidemiology and bacteriology of all the types of pneumonia are different, on initial presentation a provider is unable to distinguish between the different kinds of pneumonia until further testing is done [15]. As pneumonia is an infection that can have an unpredictable course, it is understandable that patients with pneumonia had a high rate of an unsuccessful observation care stay. It is also possible that patients with pneumonia were misdiagnosed.
We found that originating from the emergency department had a high odds of an unsuccessful observation care stay. Patients placed in observation care can range between having come from an acute encounter or a scheduled procedure and providers in the ED often lack historical information on patients [5]. The unpredictability of the type of patents presenting to the ED as well as the lack of history may lead to ED providers not understanding the complexity of care patients may need. It is important for health care administrators to be aware of these finding so that if patients do originate from the ED, they receive a more defined method of management.
Patients placed in observation care on a weekend had a higher likelihood of an unsuccessful observation care stay. This could be a result of multiple factors. Care delivered to patients on weekends does not often include the complete staff and services needed. In addition, patients may have prolonged seeing a provider until the weekend and the condition could have worsened. Although the study controlled for number of comorbidities and conditions, it was unable to account for severity of illness.
Patients in a skilled nursing facility (SNF) usually have a greater number of medical problems and require more ancillary care [16] As these patients are more “complex” it would be expected that they would have a greater likelihood of having a prolonged observation stay or requiring admission following their observation care. In the same light, it would lead to an excess in resource utilization if all patients from a SNF were admitted. Providers seeing these patients should continue to evaluate and develop a disposition plan based on need but should keep in mind that these patients have a higher likelihood of not being successful in their observation care stay.

Limitations

The study has some limitations. First, the analysis is based on data derived from claim ID, billing data, and ICD-9 codes, which are limited in that they are retrospective and can reflect incomplete coding. Second, a majority of patients who use Medicare insurance do not visit Federal hospitals, so these findings are not generalizable to Federal facilities [3]. Third, the analysis did not include information from prior year observation stays as that would require use of data from a prior year that the team did not have. Also, the files lack clinical variables such as vital signs and physical exam. The files also lack information regarding hospital characteristics such as teaching vs. non, rural vs. non, average income of hospitals, etc. Finally, the data is several years old as a result of the time it took to acquire (2 years), link and clean the files (2 years). Despite these limitations, this study provides important information regarding older Medicare beneficiaries that experience observation stay.

5. Conclusions

With the rise of observation care utilization, we assessed the factors associated with having an unsuccessful observation care stay. Patients with either an intestinal infection or pneumonia had the highest odds of an unsuccessful observation care stay. In addition, patients coming from the emergency department, placed in observation care on a weekend, or requiring a skilled nursing facility had the highest likelihood of lack of observation success. This study provides relevant and essential information for both providers and hospital administrators.

Author Contributions

G.Z.G. conceived of the study and obtained funding. C.A.S., L.-J.L., K.D. and B.D. aided in the design of the study and C.A.S. supervised the conduct of the study. L.-J.L. and D.Y.-C.H. managed the data, provided statistical advice, and conducted analyses. G.Z.G. drafted the report and all authors contributed substantially to its revision. G.Z.G. takes responsibility for the report as a whole.

Funding

This research and Gabayan were supported by the NIH/NIA Grant for Early Medical/Surgical Specialists Transition to Aging Research Grant (GEMSSTAR R03AG047862-01) and the American Geriatric Society Jahnigen Award. Sarkisian is currently supported by the NIH/NIA UCLA Resource Center for Minority Aging Research/Center for Health Improvement of Minority Elders (RCMAR/CHIME) (2P30AG081684); NIH/NIA Mid-career Award in Patient-Oriented Research (1K24AGO47899); and the NIH National Center for Advancing Translational Science (NCATS) UCLA CTSI Grant Number (UL1TR001881). The content is solely the responsibility of the authors and does not represent the official views of the NIH. None of the authors have any financial, consultant, institutional, or other conflicts of interest or relationships.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Figure A1. Study Cohort. The original sample of patients with Medicare insurance in 2013 is 52,506,598 individuals. The “Subpopulation” is the 5% sample distributed by the Centers for Medicare and Medicaid (N = 2,972,192). Of the subpopulation, patients in observation care were selected. Of the patients in observation care, the study sample was selected following the application of the exclusion criteria. Of the study sample patients, 19.1% were admitted to the hospital, 22.2% had an observation stay of 2–22 days, and 58.7% had an observation stay of 0–1 days.
Figure A1. Study Cohort. The original sample of patients with Medicare insurance in 2013 is 52,506,598 individuals. The “Subpopulation” is the 5% sample distributed by the Centers for Medicare and Medicaid (N = 2,972,192). Of the subpopulation, patients in observation care were selected. Of the patients in observation care, the study sample was selected following the application of the exclusion criteria. Of the study sample patients, 19.1% were admitted to the hospital, 22.2% had an observation stay of 2–22 days, and 58.7% had an observation stay of 0–1 days.
Healthcare 06 00138 f0a1
Table A1. CMS chronic Conditions.
Table A1. CMS chronic Conditions.
Name of Chronic ConditionVariable Name in the Dataset
Acute Myocardial InfarctionAMIc
Alzheimer’s DiseaseALZHc
Alzheimer’s Disease and Related DisordersALZH_DEMENc
Atrial FibrillationATRIAL_FIBc
CataractCATARACTc
Chronic Kidney DiseaseCHRONICKIDNEYc
Chronic Obstructive Pulmonary DiseaseCOPDc
Heart FailureCHFc
DiabetesDIABETESc
GlaucomaGLAUCOMAc
Hip/Pelvic FractureHIP_FRACTUREc
Ischemic Heart DiseaseISCHEMICHEARTc
DepressionDEPRESSIONc
OsteoporosisOSTEOPOROSISc
Rheumatoid Arthritis/OsteoarthritisRA_OAc
Stroke/Transient Ischemic AttackSTROKE_TIAc
Breast CancerCANCER_BREASTc
Colorectal CancerCANCER_COLORECTALc
Prostate CancerCANCER_PROSTATEc
Lung CancerCANCER_LUNGc
Endometrial CancerCANCER_ENDOMETRIALc
AnemiaANEMIAc
AsthmaASTHMAc
HyperlipidemiaHYPERLc
Benign Prostatic HyperplasiaHYPERPc
HypertensionHYPERTc
Acquired HypothyroidismHYPOTHc
Table A2. Diagnosis codes.
Table A2. Diagnosis codes.
DiagnosisCodes
Injuries: Sprains, fractures and joint disorders16.1 16.2 16.7
Injuries: Major trauma related: Spinal cord, Intracranial, Crushing/internal organ injury16.3 16.4 16.5
Injuries: Other including burns, wounds, poisonings, superficial injuries16.6 16.8 16.9 16.11 16.12
Symptoms: Abdominal pain17.1.7
Symptoms: Chest pain7.2.5
Symptoms: Dizziness, vertigo and syncope6.8.2 17.1.1
Symptoms: Headache6.5
Symptoms: Other symptoms, signs and ill-defined conditions17.1.2 17.1.3 17.1.4 17.1.5 17.1.6 17.1.8 17.1.9
Infection: Upper respiratory infections excluding pneumonia8.1.2 8.1.3 8.1.4 8.1.5
Infection: Intestinal Infections9.1
Infection: Urinary Tract infection and symptoms10.1.4
Infection: Other Infectious and Parasitic Diseases: Meningitis, Infective arthritis, Bacterial, Mycoses, Viral1 6.1 13.1
Infection: Skin and SubQ Infection12.1
Endocrine; nutritional; and metabolic diseases and immunity disorders3.1 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11
Diabetes with and without complications3.2 3.3
HTN7.1
Other Heart Disease: Valvular disease, Carditis7.2.1 7.2.2 7.2.6 7.2.7 7.2.10
Dysrythmias and conduction disorders7.2.8 7.2.9
Ischemic Heart Disease and MI7.2.3 7.2.4
CHF7.2.11
Circulatory Disorders: Diseases of arteries; arterioles; veins; lymphatics and capillaries7.4 7.5
Cerebrovascular Disease7.3
Diseases of the blood and blood-forming organs4
Neoplasms2
Mental Illness5
Nervous System Disorders6.2 6.3 6.4 6.6 6.7 6.8.1 6.8.3 6.9
Pneumonia 8.1.1
Other Respiratory Disease8.6 8.7 8.8 8.9
COPD8.2
Asthma8.3
Pleurisy, Pneumothorax, and Pneumonitis8.4 8.5
GI System Diseases9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 9.10 9.11 9.12
Other Renal and GU Diseases10.1.5 10.1.6 10.1.7 10.2 10.3 10.1.8
Renal Disease10.1.1 10.1.2 10.1.3
Pregnancy and childbirth related disorders11
Congenital and Perinatal Anomalies14 15
Diseases of the musculoskeletal system, skin and connective tissue12.2 12.3 12.4 13.2 13.3 13.4 13.5 13.6 13.7 13.8 13.9
Complications and Adverse events16.10
Other: Residual codes and other factors influencing healthcare17.2 18
Based on the Clinical Classification Software (CCS) Multilevel ICD-9 codes devised by the Healthcare Cost and Utilization Project (HCUP).

References

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Table 1. Observation sample characteristics.
Table 1. Observation sample characteristics.
CharacteristicTotal (N)Admitted N (%)OBS 2–30 Days N (%)OBS 0 or 1 Day N (%)
Age 1
 65–6931,2194636 (14.9)5983 (19.2)20,600 (65.9)
 70–7430,1824954 (16.4)5986 (19.8)19,242 (63.8)
 75–7929,4875583 (18.9)6368 (21.6)17,536 (59.5)
 80+63,86614,431 (22.6)15,938 (25.0)33,499 (52.4)
Gender
 Female96,76218,567 (19.2)22,577 (23.3)55,618 (57.5)
 Male57,99411,037 (19.0)11,698 (20.2)35,259 (60.8)
Race/Ethnicity 4
 White134,75325,158 (18.7)29,317 (21.8)80,278 (59.6)
 lack13,2153045 (23.0)3421 (25.9)6749 (51.1)
 Asian1885414 (22.0)420 (22.3)1051 (55.8)
 Hispanic2156538 (25.0)547 (25.4)1071 (49.7)
 North American N64592 (14.3)168 (26.0)385 (59.7)
Day of week of service
 Weekday131,48622,631 (17.2)27,549 (21.0)81,306 (61.8)
 Weekend23,2706973 (30.0)6726 (28.9)9571 (41.1)
Observation care from an ED
 NO69,0414001 (5.8)11,286 (16.3)53,754 (77.9)
 YES85,71525,603 (29.9)22,989 (26.8)37,123 (43.3)
SNF 2 utilization
 NO74,4201 (0)17,045 (22.9)57,374 (77.1)
 YES80,33629,603 (36.8)17,230 (21.5)33,503 (41.7)
Comorbidity 3
 Acute Myocardial Infarction12,8602932 (22.8)3108 (24.2)6820 (53.0)
 Alzheimer’s Disease12,8443113 (24.2)3721 (29.0)6010 (46.8)
 Alzheimer’s Disease and Related Disorders32,0607578 (23.6)9106 (28.4)15,376 (48.0)
 Atrial Fibrillation36,9467815 (21.2)9088 (24.6)20,043 (54.2)
 Cataract109,90719,547 (17.8)25,474 (23.2)64,886 (59.0)
 Chronic Kidney Disease55,21811,993 (21.7)13,873 (25.1)29,352 (53.2)
 Chronic Obstructive Pulmonary Disease56,57812,029 (21.3)14,175 (25.1)30,374 (53.7)
 Heart Failure62,98914,094 (22.4)16,040 (25.5)32,855 (52.2)
 Diabetes66,40213,334 (20.1)16,143 (24.3)36,925 (55.6)
 Glaucoma37,9326681 (17.6)8980 (23.7)22,271 (58.7)
 Hip/Pelvic Fracture91122119 (23.3)2456 (27.0)4537 (49.8)
 Ischemic Heart Disease97,14319,525 (20.1)23,272 (24.0)54,346 (55.9)
 Depression59,71911,590 (19.4)14,993 (25.1)33,136 (55.5)
 Osteoporosis43,2688067 (18.6)10,805 (25.0)24,396 (56.4)
 Rheumatoid Arthritis/Osteoarthritis101,30118,242 (18.0)24,036 (23.7)59,023 (58.3)
 Stroke/Transient Ischemic Attack35,1147997 (22.8)9170 (26.1)17,947 (51.1)
 Breast Cancer12,4491843 (14.8)2939 (23.6)7667 (61.6)
 Colorectal Cancer66471212 (18.2)1620 (24.4)3815 (57.4)
 Prostate Cancer10,1351663 (16.4)2074 (20.5)6398 (63.1)
 Lung Cancer4644789 (17.0)1119 (24.1)2736 (58.9)
 Endometrial Cancer2167345 (15.9)539 (24.9)1283 (59.2)
 Anemia100,55219,592 (19.5)24,596 (24.5)56,364 (56.1)
 Asthma27,5455612 (20.4)6807 (24.7)15,126 (54.9)
 Hyperlipidemia125,22122,660 (18.1)28,804 (23.0)73,757 (58.9)
 Benign Prostatic Hyperplasia30,0775293 (17.6)6521 (21.7)18,263 (60.7)
 Hypertension134,49425,096 (18.7)31,324 (23.3)78,074 (58.1)
 Acquired Hypothyroidism47,8569040 (18.9)11,673 (24.4)27,143 (56.7)
1 Age at observation admission. 2 Skilled Nursing Facility utilization in 2013. 3 Comorbidity based on the CMS Chronic Conditions. 4 Of race/ethnicity was, 1% was reported as “Other” and 0.4% was unknown.
Table 2. Observation sample diagnoses (N = 154,756).
Table 2. Observation sample diagnoses (N = 154,756).
CharacteristicTotal (N = 154,756)Obs 0–1 Day (N = 90,877)Admitted (N = 29,604)Obs 2–30 Days (N = 34,275)
N (%)N (%)N (%)N (%)
Diseases of the musculoskeletal system skin and connective tissue17,401 (11.2)13,278 (76.3)1095 (6.3)3028 (17.4)
Chest pain15,202 (9.8)11,283 (74.2)707 (4.7)3212 (21.1)
Neoplasms12,298 (7.9)9142 (74.3)840 (6.8)2316 (18.8)
GI System Diseases9932 (6.4)4295 (43.2)3120 (31.4)2517 (25.3)
Dizziness vertigo and syncope7439 (4.8)4244 (57.1)689 (9.3)2506 (33.7)
Other Residual codes6823 (4.4)5117 (75)302 (4.4)1404 (20.6)
Dysrhythmias and condition disorders6169 (4)3430 (55.6)1639 (26.6)1100 (17.8)
Nervous System Disorders5725 (3.7)3935 (68.7)728 (12.7)1062 (18.6)
Ischemic Heart Disease5346 (3.5)2421 (45.3)2055 (38.4)870 (16.3)
Endocrine nutritional immunity and metabolic disorders5066 (3.3)2782 (54.9)984 (19.4)1300 (25.7)
Other Renal and GU Diseases4941 (3.2)3572 (72.3)436 (8.8)933 (18.9)
Circulatory Disorders: Disease of arteries arterioles vei4547 (2.9)2420 (53.2)910 (20)1217 (26.8)
Minor Injuries4150 (2.7)1568 (37.8)1206 (29.1)1376 (33.2)
Cerebrovascular Disease3789 (2.4)1422 (37.5)1575 (41.6)792 (20.9)
Other Injuries3666 (2.4)2201 (60)301 (8.2)1164 (31.8)
Other Respiratory Disease3240 (2.1)2182 (67.3)439 (13.5)619 (19.1)
Urinary Tract Infection3218 (2.1)1014 (31.5)1320 (41)884 (27.5)
Diseases of the blood3122 (2)2007 (64.3)462 (14.8)653 (20.9)
Chronic obstructive pulmonary disease COPD3045 (2)1130 (37.1)1180 (38.8)735 (24.1)
Congestive Heart Failure2994 (1.9)871 (29.1)1476 (49.3)647 (21.6)
Complications and Adverse events2958 (1.9)1363 (46.1)922 (31.2)673 (22.8)
Other Symptoms2699 (1.7)1512 (56)252 (9.3)935 (34.6)
Hypertension HTN2459 (1.6)1581 (64.3)421 (17.1)457 (18.6)
Diabetes with and without complications2455 (1.6)1509 (61.5)360 (14.7)586 (23.9)
Other Infectious and Parasitic Diseases2343 (1.5)954 (40.7)1166 (49.8)223 (9.5)
Pneumonia1857 (1.2)444 (23.9)1077 (58)336 (18.1)
Abdominal pain1644 (1.1)914 (55.6)137 (8.3)593 (36.1)
Renal Disease1642 (1.1)471 (28.7)899 (54.8)272 (16.6)
Mental Illness1592 (1)730 (45.9)384 (24.1)478 (30)
Total sample included all patients in the study cohort: Row percents are presented. Patients with a <1% diagnosis not included.
Table 3. GEE logistic regression for unsuccessful observation care stay.
Table 3. GEE logistic regression for unsuccessful observation care stay.
Patient CharacteristicsOdds Ratio (95% CI)p
Age (REF = 65–69)
 70–741.05 (1.01–1.09)0.0066
 75–791.14 (1.1–1.18)<0.0001
 80+1.23 (1.19–1.27)<0.0001
Gender
 Male vs. Female0.92 (0.9–0.94)<0.0001
Race/Ethnicity (REF = White)
 Black1.22 (1.17–1.27)<0.0001
 Others1.06 (0.97–1.15)0.2049
 Asian/PI1.17 (1.05–1.31)0.0051
 Hispanic1.11 (1.01–1.22)0.036
Day of week of service
 Weekend vs. Weekday1.28 (1.24–1.32)<0.0001
Observation care from an ED visit
 Yes vs. No2.84 (2.74–2.95)<0.0001
Ever used SNF services in 2013
 Yes vs. No2.85 (2.68–3.02)<0.0001
 Number of chronic conditions 10.98 (0.98–0.99)<0.0001
Observation diagnosis (REF = Urinary Tract Infection)
 Intestinal Infection1.56 (1.32–1.83)<0.0001
 Pneumonia1.26 (1.13–1.41)<0.0001
 Other Infectious and Parasitic Diseases 21.13 (1.01–1.27)0.0278
 Renal Disease1.08 (0.96–1.23)0.2008
 Skin and Subcutaneous Infections1.04 (0.93–1.18)0.4759
 CHF0.97 (0.88–1.07)0.5597
 Asthma0.96 (0.82–1.13)0.6567
 Minor Injuries0.86 (0.79–0.94)0.0009
 GI system Diseases0.83 (0.76–0.89)<0.0001
 COPD0.82 (0.75–0.91)<0.0001
 Non-atherosclerotic Heart Disease0.79 (0.68–0.91)0.0012
 Non-infectious Lung Disease0.76 (0.65–0.88)0.0004
 Complications and Adverse events0.75 (0.68–0.83)<0.0001
 Ischemic Heart Disease0.73 (0.67–0.81)<0.0001
 Circulatory Disorders0.73 (0.66–0.81)<0.0001
 Cerebrovascular Diseases0.72 (0.66–0.79)<0.0001
 Mental Illness0.65 (0.57–0.74)<0.0001
 Upper Respiratory Infection0.64 (0.56–0.72)<0.0001
 Diabetes Mellitus0.62 (0.55–0.7)<0.0001
 Endocrine, nutritional, immunity and metabolic disorders0.6 (0.55–0.65)<0.0001
 Neoplasms0.59 (0.54–0.66)<0.0001
 Other Renal and GI Diseases0.58 (0.52–0.64)<0.0001
 Dysrhythmias0.53 (0.49–0.59)<0.0001
 Congenital Diseases0.53 (0.34–0.83)0.0058
 Major Injuries0.52 (0.43–0.63)<0.0001
 Nervous system Disorders0.51 (0.46–0.56)<0.0001
 Other Injuries0.49 (0.44–0.55)<0.0001
 Diseases of the musculoskeletal system, skin and connective tissue0.49 (0.45–0.53)<0.0001
 Hypertension0.48 (0.43–0.54)<0.0001
 Symptoms: Abdominal Pain0.47 (0.42–0.54)<0.0001
 Symptoms: Others0.47 (0.42–0.51)<0.0001
 Diseases of the blood0.45 (0.4–0.51)<0.0001
 Other Residual Codes0.42 (0.38–0.47)<0.0001
 Symptoms: Dizziness, Vertigo and Syncope0.38 (0.35–0.42)<0.0001
 Other Respiratory Diseases0.38 (0.34–0.42)<0.0001
 Symptoms: Headache0.32 (0.26–0.41)<0.0001
 Symptoms: Chest Pain0.17 (0.16–0.19)<0.0001
Unsuccessful Observation Care Stay defined as an observation stay that resulted in Admission or a prolonged Observation stay defined as a stay 2–30 days. 1 Number of CMS Chronic Conditions based on 0–27 conditions. 2 Including Meningitis, Infective Arthritis, Bacterial, Mycoses, Viral.

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