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Episode of Care

Episode of Care: A distinct period of care for a specific medical condition or procedure, typically encompassing all healthcare services and interventions provided during that time.
This term describes the continuum of care, from initial presentation to final resolution or stabilization of a health issue.
Analyzing Episode of Care data can help optimize resource utilization, improve patient outcomes, and enhance the overall quality of healthcare delivery.
Reserchers can use this concept to study patterns, costs, and effectiveness of medical treatments over a defined timeframe.

Most cited protocols related to «Episode of Care»

The first step in non-fatal estimation was the compilation of data sources from systematic data and literature searches conducted by cause. This process resulted in 4043 published studies newly included in GBD 2016, leading to a total of 14 521. Our network of collaborators for GBD 2016 provided 2598 data sources and studies. These were systematically screened, together with sources suggested by country-level experts, surveys located in multinational survey data catalogues, and Ministry of Health and Central Statistical Office websites. We analysed 18 792 sources of epidemiological surveillance data (country-years of disease reporting), up from 14 081 in 2015. All counts reflect our updated counting criteria for GBD 2016. The supplementary methods provides details of data adjustments, correction factors, and standardisations employed in incorporating these different data types (appendix 1, p 18).
The number of location-years of hospital inpatient data by cause increased from 1176 in GBD 2015 to 3557 in GBD 2016. This increase can be attributed to the addition of new years of data for some locations, as well as newly incorporated data for 16 countries where we had previously lacked clear information about the population covered. To allow their use in GBD, we first collated information from surveys and hospital administrative records to estimate hospital admission rates per capita for all GBD locations by age and sex, from 1990 to 2016, using DisMod-MR 2.1 (appendix 1, p 7). We then used inpatient data by cause from locations with unclear denominators as cause fractions of the all-cause inpatient admission rates. Three adjustment factors were derived from USA health insurance claims data on more than 80 million person-years of coverage. The first factor corrected for multiple inpatient episodes for the same cause in an individual. The second adjustment was to include secondary diagnostic fields. The third adjustment was to include any mention of a cause in inpatient or outpatient episodes of care as opposed to inpatient episodes with a primary diagnosis only. This new method of predicting prevalence or incidence from inpatient data allowed us to use these sources for 16 more causes than in 2015. The supplementary methods provides a detailed description of our process for inpatient data (appendix 1, p 11).
To provide a summary view on data availability, the number of causes at the most detailed level for which we have any prevalence or incidence data from 1980 to 2016 by location is presented in the appendix (appendix 1, p 722). An online search tool is available to view all data sources that were used in the estimation process for each cause.
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Publication 2017
Diagnosis Episode of Care Head Health Insurance Inpatient Outpatients
This study compared the ADAMS [7 (link),8 (link)] dementia diagnosis with Medicare claims records to assess the sensitivity and specificity of Medicare claims to identify true disease as well as to identify the agreement between these two sources of diagnostic information. December 31, 2003 was the date of comparison for Medicare claims records and ADAMS dementia assessment. Persons were classified as having dementia or not based on Medicare claims as of that date, and were also assessed as having dementia or not as of that date. Further, we compared the age of dementia onset, as estimated in ADAMS, to the age of a subject when they first had a Medicare claim that noted dementia. In such cases, persons were categorized as having their initial Medicare claim denoting dementia more than one year prior to dementia onset as estimated in ADAMS; occurring within a year of ADAMS onset; occurring more than one year after ADAMS onset.
Medicare claims records are generated when beneficiaries receive care financed through the program. Such records note not only payment information, but also the date that care was received, diagnosis code information (ICD-9-CM codes) for one primary diagnosis, and several secondary diagnoses. Some files (inpatient hospital claims), have up to 9 secondary diagnosis codes in addition to the primary diagnosis, while others (part B physician supplier claims), have only 3. This study uses the ICD-9-CM codes used in past work to identify dementia in Medicare claims (Appendix 1) [19 (link)]. We also conducted sensitivity analyses by adding several additional ICD-9-CM codes suggested by colleagues associated with the ADAMS dementia assessment, but the differences were trivial (e.g., 4 additional cases of dementia identified in claims) so results are shown using the ICD-9-CM codes that were used in past work to increase comparability [20 (link)]. All available Medicare claims records were used to complete the study; inpatient, outpatient; part B physician supplier file; home health; Skilled Nursing Facility (SNF); hospice; and durable medical equipment. Persons having a claim with at least one of the codes (in any position, primary or secondary) listed in the appendix were classified as having dementia. Separate analyses were run for dementia of the AD type, which was defined in Medicare claims by the presence of ICD-9-CM code 331.0.
Cost to the Medicare program was defined as the amount that Medicare actually paid for an episode of care and using all files as noted above, following past work in this area [9 (link),11 (link),12 (link)].
Publication 2009
Diagnosis Durable Medical Equipment Episode of Care Hospice Care Hypersensitivity Inpatient Outpatients Physicians Presenile Dementia
The algorithm was developed in a dataset from the NIVEL Primary Care Database (NIVEL-PCD), including a representative sample of 219 general practices covering a total population of 867,140 listed patients [14 ]. NIVEL-PCD collects data from routine EHR systems including consultations, morbidity, prescriptions, and diagnostic tests. Diagnoses are recorded using the ICPC-1 coding system (Multimedia Appendix 1) [8 ]. All general practices in the sample had sufficient data quality over the period 2010-2012, fulfilling the following criteria: at least 500 listed patients, complete morbidity registration (defined as 46 or more weeks per year; this is, a year minus a maximum of six weeks’ holidays), and sufficient ICPC coding of diagnostic information (defined as 70% or more of recorded encounters with an ICPC code) [15 ]. Morbidity data used included ICPC-coded episodes of care, encounters, and diagnosis-coded prescriptions.
Dutch law allows the use of extractions of EHRs for research purposes under certain conditions. According to Dutch legislation, obtaining neither informed consent nor approval by a medical ethics committee is obligatory for this kind of observational study [16 ].
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Publication 2019
Diagnosis Episode of Care Ethics Committees Patients Prescriptions Primary Health Care Tests, Diagnostic
The four continuity measures - the Bice-Boxerman Continuity of Care (COC) Index,14 (link) Herfindahl Index (HI), usual provider of care (UPC),15 (link) and Sequential Continuity of Care Index (SECON)16 (link) —are described in Table 1.
The COC index reflects “the extent to which a given individual’s total number of visits for an episode of illness or a specific time period are with a single or group of referred providers.”14 (link) The HI, which is most commonly used in economic analyses of market concentration, is similar to the COC index in that it reflects the extent to which an individual’s visits during an episode of care are concentrated with a single or group of providers. Although conceptually similar to the COC index, it is calculated using a different mathematical formula. Both measures sum the squared number of visits to a given providers. UPC reflects the “density” of care, or the extent to which visits are concentrated with a single usual provider or group of providers during an episode.11 (link) It equals the number of visits to the provider or practice group with the highest number of visits divided by the total number of visits. SECON varies from the others in that it considers the order of visits, not just their concentration or dispersion among providers. It equals the fraction of sequential visits pairs at which the same provider is seen, i.e. same provider being seen at both the previous and current visits.
We limited the calculation of these measures to outpatient evaluation and management visits defined as Berenson-Eggers Type of Service codes M1A, M1B, M4A, M4B, M5C, M5D, and M6. Only a single E&M visit per day for each patient-provider dyad was counted, where providers were determined using the National Provider Identifier. Visits that were related to complications, hospitalizations, or emergency department visits were excluded from our calculation of the COC index. In addition, we counted only visits to those clinicians that were most likely to be involved in outpatient management for each of the three conditions. For CHF, this included primary care providers (PCPs - general practitioners, family practitioners, internal medicine without subspecialty training, and nurse practitioners), cardiologists, and pulmonologists. For COPD, we included PCPs and pulmonologists; for DM, we included PCPs, cardiologists, endocrinologists, podiatrists, and ophthalmologists. Physician specialty was determined using the specialty code from the Carrier file. With the exception of general practitioners, each specialty class of provider accounted for more than 2% of outpatient E&M visits, and the included providers accounted for 90.6% of total outpatient E&M visits for CHF, 89.6% for COPD, and 86.0% for DM. Practice groups were defined using the tax identification number assigned to each outpatient evaluation and management claim for the above provider types. Each measure was constructed separately using visits to providers and to practice groups.
Publication 2014
BMP1 protein, human Cardiologists Chronic Obstructive Airway Disease Continuity of Patient Care Endocrinologists Episode of Care General Practitioners Hospitalization Ophthalmologists Outpatients Patients Physicians Practitioner, Nurse Primary Health Care Pulmonologists Vision
To qualify for inclusion in this study cohort, patients with AMI must have had a principal hospital discharge diagnosis of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 410.xx, excluding those with 410.x2 (AMI, subsequent episode of care) at the discharging hospital. We defined an index admission as an admission in which we evaluated the subsequent 30 days after discharge for a readmission. A single patient may have >1 index admission in the study year. We excluded patients aged <65 years, those who died during their index hospitalization, and those admitted and discharged on the same day. If a patient had ≥1 AMI admission within 30 days of discharge from an index AMI admission, we did not consider the additional AMI admissions as index admissions; they were considered as readmissions. Thus, any AMI admission was either an index admission or a readmission but not both. We considered admissions with transfer as a single episode of care, with the codes to define the variables in the model derived from the discharging hospital. The readmission was always attributed to the discharging hospital. To maximize our ability to risk adjust and to identify readmissions, we restricted the cohort to patients enrolled in fee-for-service Medicare Parts A and B for 12 months before their AMI hospitalization who continued in the fee-for-service plan for at least 30 days after discharge.
Publication 2011
Diagnosis Episode of Care Hospitalization Patient Discharge Patients Thirty Day Readmission

Most recents protocols related to «Episode of Care»

This study is based on the English National Health Service (NHS) Hospital Episode Statistics (HES) database, linked to Office of National Statistics (ONS) official mortality data and pseudonymised for the purposes of research. The data was obtained via application to the NHS Digital Data Access Request Service (DARS) approved on 6/4/2017, DARS reference NIC 33318. Specifically, we obtained extracts of individual patient data from the admitted patient care (APC) dataset within HES for the financial years (FYs; UK government accounting years, which run from April 1st to the following March 31st) 2010/11, 2012/13 and 2016/17. The APC dataset contains detailed data on all admissions to NHS hospitals in England [24 ]. Professional coders enter the data retrospectively using information collected from the medical records. Up to 20 diagnosis fields are used to capture the primary diagnosis for each admission plus other relevant diagnoses, using ICD-10 codes. We obtained diagnosis data for every FY from 2005/6 to 2016/17. Following the method of previous studies [25 ], for each admission the patient was classified as having comorbid dementia if their record included an ICD10 dementia code as secondary diagnosis for that admission, or as a primary or secondary diagnosis for any previous admission at any hospital over the prior 5 FYs. For this purpose we adapted the set of ICD-10 codes used by the NAD survey in 2016/17 (See Box A1 in S1 File) [26 ]. Individuals coded as having dementia at any point in a FY were assumed to have that status for any other admission during that FY. We had no ability at any point to identify specific individuals within the datasets.
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Publication 2023
Diagnosis Episode of Care Fingers Health Services, National Patients Presenile Dementia
We used the Clinical Practice Research Datalink (CPRD) Aurum15 (link) January 2022 dataset, with individual level linked data from Hospital Episode Statistics Admitted Patient Care (HES APC), Office for National Statistics (ONS) deaths, Second Generation Surveillance System (SGSS) SARS-CoV-2, and COVID-19 Hospitalisations in England Surveillance System (CHESS).16
The CPRD Independent Scientific Advisory Committee (application 20_000135) and the London School of Hygiene and Tropical Medicine (LSHTM) Ethics Committee (application 22717) approved the study. CPRD provided relevant HES, ONS, SGSS and CHESS data for the study population. All code lists are published on LSHTM Data Compass.17
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Publication 2023
COVID 19 Episode of Care Ethics Committees Hospitalization SARS-CoV-2
The NPR and the CDR are maintained by Swedish National Board of Health and Welfare and collect individual patient data including primary cause of admission, co-morbidities, health care episodes and date of death. Data collection is based on social security numbers assigned to all registered residents in Sweden. The NPR includes all in-patient care in Sweden since 1987, the register has been validated several times and the completeness of the reported data is almost 100% [22 ]. Data on Charlson comorbidity index (CCI) in our study was obtained from the NPR and data on 30-day survival was obtained from the CDR.
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Publication 2023
Episode of Care Patients

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Publication 2023
Diagnosis Episode of Care Patients Primary Health Care Signs and Symptoms
Data on infant deaths (within the first year of life) are provided within birth registers for Finland and Nova Scotia. Data are available for deaths up to 12 years of age in both Scotland and Iceland, by linkage of birth registers with national death registers. No data on infant or child deaths were available from the Rabin Medical Center database.
In addition to comprehensive pregnancy, maternity and neonatal data provided by each country, additional data on child health and development, and inpatient and outpatient hospital attendances, are available for Finland, Iceland, and Scotland, from various sources. Live births in Finland were linked to hospital records of inpatient admissions and outpatient attendances from 2006 to 2018, which provide information on a range of diagnoses, including neurodevelopmental disorders and physical conditions. In Iceland, live births were linked with childhood diagnoses of neurodevelopmental and behavioural disorders from 2010 to 2018, provided by patient registers from specialised centres which provide care, counselling, and follow-up for children with these conditions.
Scottish live births were linked with child health and development data up to 5 years old, supplied by the records from the Child Health Systems Programme (CHSP) Pre-School information system. The CHSP Pre-School system supports the delivery of the Child Health Programme, a universal health promotion programme offered to all children and their families, by facilitating the recording of health data obtained at a series of preschool reviews. These assessments are undertaken at prespecified milestones to evaluate children’s health, growth, and development, and are typically conducted by health visitors (nurses or midwives with specialist knowledge and training in community public health nursing, who provide support and advice for all families until a child starts school). These record linkages will enable analysis of long-term neurodevelopmental and behavioural outcomes for a significant subset of children in the Co-OPT ACS cohort. In the future, Scottish Morbidity Records for episodes of care (day-case and inpatient) within acute specialties (SMR01), cancer services (SMR06) and mental health services (SMR04) will be linked with live births in Scotland in the Co-OPT ACS cohort, to enable evaluation of longer-term benefits and safety of ACS in children using ICD-defined (International Statistical Classification of Diseases and Related Health Problems) outcome criteria, such as childhood infection.
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Publication 2023
Behavior Disorders Child Child, Preschool Childbirth Children's Health Diagnosis Episode of Care Health Visitors Infant Infant, Newborn Infection Inpatient Linkage, Genetic Malignant Neoplasms Mental Health Services Midwife Neurodevelopmental Disorders Nurses Obstetric Delivery Outpatients Patients Physical Examination Pregnancy Safety Wellness Programs

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More about "Episode of Care"

Episode of Care (EoC) is a crucial concept in healthcare that encompasses the continuum of care, from initial presentation to final resolution or stabilization of a health issue.
This term describes the distinct period of care for a specific medical condition or procedure, including all the healthcare services and interventions provided during that time.
Analyzing Episode of Care data can help optimize resource utilization, improve patient outcomes, and enhance the overall quality of healthcare delivery.
Researchers can leverage this concept to study patterns, costs, and effectiveness of medical treatments over a defined timeframe.
Synonyms and related terms for Episode of Care include: Care Episode, Healthcare Episode, Treatment Episode, and Medical Episode.
Abbreviations such as EoC and HE are also commonly used.
Key subtopics within Episode of Care include: - Continuum of Care: The seamless progression of healthcare services, from initial presentation to final resolution or stabilization of a health issue. - Resource Utilization: Examining the efficient and effective use of healthcare resources, such as personnel, facilities, and equipment, during an Episode of Care. - Patient Outcomes: Evaluating the impact of healthcare interventions on the patient's health and wellbeing during an Episode of Care. - Healthcare Quality: Assessing the overall quality of healthcare delivery and identifying areas for improvement within an Episode of Care.
Researchers can leverage statistical software like SAS version 9.4, SPSS Statistics, Stata 15, and Stata 14 to analyze Episode of Care data and uncover valuable insights.
Additionally, tools like Vacutainer® PST tubes and ICNet can aid in data collection and management during an Episode of Care.
By understanding the concept of Episode of Care and its related subtopics, healthcare professionals and researchers can optimize protocols, enhance reproducibility, and improve the overall quality of healthcare delivery.
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