Israel IL: Hospital Beds: per 1000 People data was reported at 3.300 Number in 2012. This records a decrease from the previous number of 3.370 Number for 2011. Israel IL: Hospital Beds: per 1000 People data is updated yearly, averaging 6.140 Number from Dec 1960 (Median) to 2012, with 31 observations. The data reached an all-time high of 8.079 Number in 1975 and a record low of 3.300 Number in 2012. Israel IL: Hospital Beds: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Israel – Table IL.World Bank.WDI: Health Statistics. Hospital beds include inpatient beds available in public, private, general, and specialized hospitals and rehabilitation centers. In most cases beds for both acute and chronic care are included.; ; Data are from the World Health Organization, supplemented by country data.; Weighted average;

Hospital Beds in Israel increased to 3.09 per 1000 people in 2024 from 3.01 per 1000 people in 2023. This dataset includes a chart with historical data for Israel Hospital Beds.

Additional file 1: Table S1. Rate ratios of discharges of SMI group compared to total population, 2016–2019, with 95% CI, by age and diagnoses, and by sex for total discharges. Table S2. Standardized discharge ratios (SDR) for SMI group compared to total population, aged 18–74, with 95% CI, by period of discharge, sex and diagnoses. Table S3. Standardized discharge ratios (SDR) for SMI group compared to total population, aged 18–74, with 95% CI, by period of discharge, type of admission and hospital ownership.

Background Payments to healthcare providers create a set of economic incentives that influence their behavior, considerations and decision-making. Diagnosis related groups (DRGs) incentivize hospitals to (a) increase the number of cases; (b) increase the income per patient; and (c) reduce costs per patient. For each incentive there may be positive and negative consequences. For example, increasing the number of cases is a positive consequence if the procedures are clinically desirable and appropriate, and if they contribute to reducing waiting times and improving the efficiency of the hospital. However, if the hospital carries out unnecessary or inappropriate procedures, the financial incentive produces a negative result. Responses to economic incentives have important consequences on the patients’ clinical outcomes and the providers’ financial stability. A principal-agent relationship occurs when one individual (the principal) engages another (the agent), and delegates decision-making power to the agent to perform a service on its behalf. Physicians are perfect agents if they are fully committed to their patients. However, the reality is more complex, and physicians have a range of commitments and considerations, such as their own financial well-being or their organizations’ objectives of fiscal equilibrium or profit maximization. Physicians and hospital managers are employed by hospitals and are supposed to act in the interest of their employer. At the same time, they are consulted by patients to make treatment decisions on their behalf. Thus, hospital professionals are “dual agents” because they must reconcile patients’ clinical needs and the best treatment available with economic considerations. These simultaneous commitments are sometimes compatible, but may collide or create dilemmas. In some situations, these may result in distorted decision-making or professional burnout, but in others, professionals may be able to fulfill at least some of these considerations. When handled poorly, misaligned considerations may lead to improper care or unbalanced finances. In 2010, the Israel Ministry of Health intensified the adoption of a local variant of DRGs, known as Procedure-Related Group (PRGs) based payments. The main difference between the methods is that while DRGs classify patients based on the main diagnosis, PRGs classify them based on the main procedure performed, and are not adjusted to case-mix or severity of case like DRGs. The PRG reform opened a window of opportunity to study these new economic incentives, and how they impact hospital activities and health professionals’ considerations, decision-making, and behavior. The reform created a "natural experiment" that enabled comparison between "before with per diem payments" and "after with PRGs" at various levels. It also made possible a set of interesting comparisons between the Israeli PRG and other countries' DRG-based payments. Objectives This dissertation had four main objectives: 1. To analyze the effects of the PRG reform on hospital activities, as measured by the number of patients and lengths of stay at the ward level. 2. To examine the impact of the introduction of PRG-based payments on average length of stay (ALoS) at the procedure level. 3. To examine the impact of PRG payments from the perspectives of hospital professionals (managers and physicians). Specifically, the goal was to explore which economic incentives were created by the reform, how it affected admission and treatment decision-making, clinical practice, what changes occurred and how hospitals as organizations responded to the payment reform. 4. To examine how hospital professionals work to balance and reconcile clinical and economic considerations in their decision-making in two countries with activity-based payment systems. Methods A mixed-methods design was applied. Sections ‎5.1 and 5.2, which are quantitative, examined the effect of the adoption of PRGs on hospital activity as measured by number of patients and ALoS. We analyzed inpatient data provided by the Ministry of Health from all 29 public hospitals in Israel between 2005 and 2016. In section ‎5.1, the observations were made at the level of hospital wards, as proxies for clinical fields. We used difference-in-differences analyses, where the intervention group was surgical wards for which many PRG codes were created between 2010-2013, and the control group was surgical wards for which no PRG codes were created. In section 5.2 the unit of analysis was 14 procedures. We employed a mixed-effects duration analysis approach to address the strictly non-negative and right-skewed ALoS data. We opted for a Bayesian approach to estimate the relative change in ALoS. Since a quantitative analysis may not capture all the impacts of the payment reform, we expanded the data collection and analysis by applying a qualitative approach. In sections ‎5.3 and ‎5.4 we examined the perspectives of hospital professionals in terms of the effects of the PRGs’ economic incentives and impact. Data were collected through semi-structured in-depth interviews with hospital directors (chief executive officers [CEOs] and chief financial officers [CFOs]), chief physicians and physicians in five Israeli and five German hospitals, purposefully sampled by maximum variation. The interviews were conducted between December 2017 and August 2018 in Israel, and between March and August 2019 in Germany. We interviewed 33 hospital professionals from Israel and 13 from Germany. We used thematic analysis that also involved intercoder reliability and triangulation. Results The findings detailed in section ‎5.1 revealed that discharges increased more in the control group wards (surgical wards for which no new PRG code was created) than in the intervention wards as a group (surgical wards where many new PRG codes were created). However, a more in-depth analysis of each intervention ward separately indicated that discharges increased in some, but decreased in other wards. The ALoS decreased more in intervention wards from 3.85 to 3.59 days, which represents 6%, compared to 1% in the control group. Difference-in-differences results suggested no causality between the PRG payment reform and changes in inpatient activity. The study reported in Section 5.2 showed that when refining the unit of analysis to procedures (instead of wards), the changes became more visible. Length of stay declined in half of the procedures analyzed, and in particular in six out of seven urological procedures. In these procedures, there was a 14% average reduction in ALoS, which ranged from 11% to 20%. In section ‎5.3, hospital professionals reported that the payment reform led to organizational changes such as increased transparency due to better reporting of activity and enhanced supervision of activities by the MoH and hospital managers. The interviewees also reported several steps taken in response. These included (1) shifting activities to afterhours and using operating rooms more efficiently to enable increased surgical activity; (2) reducing costs by shortening lengths of stay, as well as cost-consciousness in procurement; and (3) increasing revenues by improving coding and a more judicious selection of procedures. Respondents also reported moderating factors that reduced the effects of the reform. For example, organizational factors such as the public nature of hospitals or the (un)availability of healthcare resources did not always allow hospitals to increase the number of cases treated. In addition, conflicting incentives such as multiple payment mechanisms or the underpricing of procedures tended to blur the incentives of the reform. Finally, managers and physicians noted that they have many other considerations that outweigh economic issues. Compared to managers, clinicians were more careful with the economic incentives in their daily work. Extending the latter conclusion in section ‎5.3, section ‎5.4 reports on how hospital professionals, as “dual agents” attempt to re-equilibrate misaligned considerations in Israel and Germany. The focus was on dilemmas between clinical and economic considerations, and strategies to mitigate them. Hospital professionals report many situations in which activity-based payment incentives, proper treatment, and clinical and economic considerations are aligned. In this case, efficiency can be improved; e.g., by curbing unnecessary expenditures, or specializing in certain procedures. When considerations misalign, the hospital professionals identified a range of strategies that can contribute to reducing dilemmas in decision-making or reconciling competing considerations. These included ‘reshaping management’, such as planning the treatment ahead and improving the coding, and ‘reframing decision-making’, which involves working with averages and developing toolkits for decision-making. Discussion In this dissertation, we evaluated the direct and indirect impact of PRGs on providers; namely, both the hospital as an organization and its professionals. Through a case study of the hospital payment reform in Israel, we analyzed the economic incentives created and subsequent responses at different levels: the ward, the procedure, the manager and the physician. The quantitative analysis did not suggest causality between the PRG payment reform and changes in inpatient activity at the ward level. However, a more fine-grained analysis at the procedure level found decreases in ALoS in half of the procedures under consideration. This might have freed resources to treat more patients, which may have reduced waiting times. It may have been easier to reduce ALoS in the urological procedures since these had relatively long initial ALoS. The factors that may have hampered the effects of the reform as measured by the number of patients and ALoS emerged to reflect inadequate pricing of procedures, and

Financial overview and grant giving statistics of Beth Israel Deaconess Hospital Plymouth Inc

Mortality rate of sepsis cases during hospitalization (%) by age group.

Background: Since 2010, Israel has expanded the adoption of procedure-related group (PRG) based payments for hospitals. While there is a rich quantitative literature that assesses the effects of payment reforms on efficiency or quality of care, very few qualitative studies have focused on the impacts of diagnosis-related group (DRG)-like payments on hospitals from the perspective of hospital workers as change agents. Methods: We used a qualitative, thematic analysis based on 33 semi-structured in-depth interviews with chief executive officers (CEOs), chief financial officers (CFOs), ward directors and physicians conducted in five public hospitals in Israel, sampled by maximum variation according to hospital characteristics. Results: Interviewees reported that the payment reform led to organizational changes such as increased transparency and enhanced supervision. Interviewees also reported several actions in response to the economic incentives of PRGbased payment. These included (1) shifting activities to afterhours and using operating rooms (ORs) more efficiently to enable increased surgical volumes; (2) reducing costs by shortening lengths of stay and increasing cost-consciousness in procurement; and (3) increasing revenues by improving coding and selecting procedures. Moderating factors reduced the effects of the reform. For example, organizational factors such as the public nature of hospitals or the (un)availability of healthcare resources did not always allow hospitals to increase the number of cases treated. Also, conflicting incentives such as multiple payment mechanisms or underpricing of procedures blurred the incentives of the reform. Finally, managers and physicians have many other considerations that outweigh the economic ones. Conclusion: PRG payments affected the organizational dynamics of hospitals and changed decision-making about admission and treatment policies. However, such effects were moderated by many other factors that should be considered when shaping and analyzing hospital payment reforms.

Financial overview and grant giving statistics of Beth Israel Deaconess Medical Ctr & Childrens Hospital Medical Care Co

ABSTRACT BACKGROUND: Since February 2020, data on the clinical features of patients infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and their clinical evolution have been gathered and intensively discussed, especially in countries with dramatic dissemination of this disease. OBJECTIVE: To assess the clinical features of Brazilian patients with SARS-CoV-2 and analyze its local epidemiological features. DESIGN AND SETTING: Observational retrospective study conducted using data from an official electronic platform for recording confirmed SARS-CoV-2 cases. METHODS: We extracted data from patients based in the state of Pernambuco who were registered on the platform of the Center for Strategic Health Surveillance Information, between February 26 and May 25, 2020. Clinical signs/symptoms, case evolution over time, distribution of confirmed, recovered and fatal cases and relationship between age group and gender were assessed. RESULTS: We included 28,854 patients who were positive for SARS-CoV-2 (56.13% females), of median age 44.18 years. SARS-CoV-2 infection was most frequent among adults aged 30-39 years. Among cases that progressed to death, the most frequent age range was 70-79 years. Overall, the mortality rate in the cohort was 8.06%; recovery rate, 30.7%; and hospital admission rate (up to the end of follow-up), 17.3%. The average length of time between symptom onset and death was 10.3 days. The most commonly reported symptoms were coughing (42.39%), fever (38.03%) and dyspnea/respiratory distress with oxygen saturation < 95% (30.98%). CONCLUSION: Coughing, fever and dyspnea/respiratory distress with oxygen saturation < 95% were the commonest symptoms. The case-fatality rate was 8.06% and the hospitalization rate, 17.3%.

BackgroundWe evaluated published evidence (2012–2022) on pediatric RSV burden in 149 countries within World Health Organization (WHO) regions of Africa (AFRO), Americas (AMRO, excluding Canada and the USA), Eastern Mediterranean (EMRO), Europe (EURO, excluding European Union countries and the UK), Southeast Asia (SEARO), and Western Pacific (WPRO, excluding Australia, China, Japan, New Zealand, and South Korea).MethodsGap analysis on RSV-associated disease (hospitalizations, hospital course, mortality or case fatality, detection, and incidence) in children ≤2 years old, where hospitalization rates, hospital course, mortality rate, case fatality rate (CFR), and postmortem detection rates were summarized, by region, for each country.ResultsForty-two publications were identified covering 19% of included countries in AFRO, 18% in AMRO, 14% in EMRO, 15% in EURO, 18% in SEARO, and 13% in WPRO. Methods, case definitions, and age groups varied widely across studies. Of these 42 publications, 25 countries reported hospitalization rate, hospital course, mortality rate, CFR, and/or postmortem detection rate. RSV hospitalization rate (per 1,000 children per year/child-years) was higher among ≤3-month-olds (range, 38 in Nicaragua to 138 in the Philippines) and ≤6-month-olds (range, 2.6 in Singapore to 70 in South Africa) than in 1–2-year-olds (from 0.7 in Guatemala to 19 in Nicaragua). Based on 11 studies, in AFRO (South Africa), AMRO (Chile and Mexico), EMRO (Lebanon and Jordan), EURO (Israel and Turkey), and SEARO (India), hospitalized children ≤2 years old remained hospitalized for 3–8 days, with 9%–30% requiring intensive care and 4%–26% needing mechanical ventilation. Based on a study in India, community-based CFR was considerably higher than that in the hospital (9.1% vs. 0% in ≤3-month-olds; 7.1% vs. 2.8% in ≤6-month-olds).ConclusionsNational and regional heterogeneity of evidence limits estimates of RSV burden in ≤2-year-olds in many WHO region countries, where further country-specific epidemiology is needed to guide prioritization, implementation, and impact assessment of RSV prevention strategies.

A beginner-friendly version of the MIT-BIH Arrhythmia Database, which contains 48 electrocardiograms (EKGs) from 47 patients that were at Beth Israel Deaconess Medical Center in Boston, MA in 1975-1979.

Update (7/18/2025)

This data was updated to a new format on 7/18/2025 with new filenames. Now heartbeats are labeled and their annotations are in new CSV and JSON files. This means that each patient's EKG file is now named {id}_ekg.csv and they have accompanying heartbeat annotation files, named {id}_annotations.csv. For example, if your code used to open 100.csv, it should be changed to opening 100_ekg.csv.

Filenames

Each of the 48 EKGs has the following files (using patient 100 as an example): - 100_ekg.csv - a 30-minute EKG recording from one patient with 2 EKG channels. This also contains annotations (the symbol column), where doctors have marked and classified heartbeats as normal or abnormal. - 100_ekg.json - the 30-minute EKG with all of its metadata. It has all of the same data as the CSV file in addition to frequency/sample rate info and more. - 100_annotations.csv - the labels for the heartbeats, where doctors have manually classified each heartbeat as normal as one of dozens of types of arrhythmias. There may be multiple of these files (number 1, 2, or 3), since the original MIT-BIH Arrhythmia Database had multiple .atr files for some patients. The MIT-BIH DB did not elaborate on why, though the differences between each annotation file seems to be only a few lines at most. - 100_annotations.json - the annotation file that is as close to the original as possible, keeping all of its metadata, while being an easy to use JSON file (as opposed to an .atr file, which requires the WFDB library to open).

Other files: - annotation_symbols.csv - contains the meanings of the annotation symbols

There are 48 EKGs for 47 patients, each of which is a 30-minute echocardiogram (EKG) from a single patient. (Record 201 and 202 are from the same patient). Data was collected at 360 Hz, meaning that 360 data points is equal to 1 second of time.

Each file's name starts with the ID of the patient (except for 201 and 202, which are the same person).

Related Data

The P-waves were labeled by doctors and technicians, and their exact indices are available in the accompanying dataset, MIT-BIH Arrhythmia Database P-wave Annotations.

How to Analyze the Heart with Python

1. How to Analyze Heartbeats in 15 Minutes with Python
2. How the Heart Works (and What is a "QRS" Complex?)
3. How to Identify and Label the Waves of an EKG
4. How to Flatten a Wandering EKG
5. How to Calculate the Heart Rate

What is a 12-lead EKG?

EKGs, or electrocardiograms, measure the heart's function by looking at its electrical activity. The electrical activity in each part of the heart is supposed to happen in a particular order and intensity, creating that classic "heartbeat" line (or "QRS complex") you see on monitors in medical TV shows.

There are a few types of EKGs (4-lead, 5-lead, 12-lead, etc.), which give us varying detail about the heart. A 12-lead is one of the most detailed types of EKGs, as it allows us to get 12 different outputs or graphs, all looking at different, specific parts of the heart muscles.

This dataset only publishes two leads from each patient's 12-lead EKG, since that is all that the original MIT-BIH database provided.

What does each part of the QRS complex mean?

Check out Ninja Nerd's EKG Basics tutorial on YouTube to understand what each part of the QRS complex (or heartbeat) means from an electrical standpoint.

Columns

• index
• the first lead
• the second lead

The two leads are often lead MLII and another lead such as V1, V2, or V5, though some datasets do not use MLII at all. MLII is the lead most often associated with the classic QRS Complex (the medical name for a single heartbeat).

Patient information

Info about [each of the 47 patients is available here](https://physionet.org/phys...

These are 10-hour electrocardiograms (EKGs, or electrical recordings of heartbeats) from 23 patients. These were recorded as part of a joint effort between MIT and Beth Israel Hospital in Boston, MA, and are one of dozens of datasets with electrocardiogram data.

These EKGs are CSVs of voltage data from real hearts in real people who have atrial fibrillation. Atrial fibrillation (or "A-fib") is when the heartbeat doesn't happen on a steady rhythm, because the natural pacemaker cells of the heart are disorganized and exhibit "fibrillations" (a sort of physiological quivering).

Files

Each patient has 6 files:

• 01234_ekg.csv - The 10-hour electrocardiogram as two channels of voltage measurements (millivolts), with the locations of QRS annotations and other generic annotations as additional columns
• 01234_ekg.json - The 10-hour electrocardiogram, plus metadata, like sample rate, etc.
• 01234_annotations.csv - The locations of miscellaneous annotations made by doctors or EKG technicians. See annotation_symbols.csv for the annotations' meanings.
• 01234_annotations.json - The same data as 01234_annotations.csv in addition to metadata
• 01234_qrs.csv - The locations of QRS complexes (i.e. heartbeats) as labeled by doctors and EKG technicians.
• 01234_qrs.json - The same data as 01234_qrs.csv in addition to metadata

Generally, the .csv files have just the voltage data or the locations of QRS complexes or another annotations. The .json files have all of that data in addition to metadata (such as sample rate, ADC gain, and more).

To get started, you will probably want the *_ekg.csv files. Check out our example notebook for how to open and graph the data.

The files for patients 00735 and 03665 were excluded, because the original dataset did not have their EKGs, only the QRS annotations and miscellaneous annotation files. You can download those files here.

Notebooks

• How to open and graph the heartbeat data.
• How to find peaks and valleys in digital signals (includes non-EKG data)
• How to smooth and filter digital signals (includes non-EKG data)

Sample rate

The data was collected at 250 Hz (or 250 samples per second) at a resolution of 12-bits. This means that if you get the first 250 elements from the EKG array, you have 1 second of heartbeat data.

What is a "QRS complex"?

A "QRS complex" is the big spike in the classic heartbeat blip that you may see on your smartwatch or in a hospital show on TV.

In this dataset, doctors and EKG technicians have labeled the locations of the complexes, and by extension the location of each heartbeat. This can help you not only identify Q, R, and S waves right away, but also help feed these heartbeats into hand-written or machine learning algorithms to start identifying and classifying heartbeats--though this only one of many datasets you might want to train an algorithm on, since there are phundreds of types of arrhythmias](https://litfl.com/ecg-library/diagnosis/) (or "bad" heart rhythms).

The EKG channels

Typically, electrocardiogram datasets will specify which channels from the 12-lead EKG that the data came from. For example, the EKG for patient 100 from our other MIT-BIH Arrhythmia Database dataset came with two channels: Lead II and V5. Other EKGs in the many MIT-BIH EKG datasets may have channels Lead I and V4, or Lead II and V2, and so on.

For whatever reason, the channels in the original Atrial Fibrillation dataset are not labeled with the actual 12-lead EKG channels that they came from. If you are able to find the names of the channels in the MIT-BIH archives, please leave a comment so that we can improve this dataset. Knowing the channel names means we can then know which part of the heart (and which specific muscle) is giving off the signal. Thanks!

Citations

Moody GB, Mark RG. A new method for detecting atrial fibrillation using R-R intervals. Computers in Cardiology. 10:227-230 (1983).

Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a ne...

Baseline characteristics of hospitalized severe or critical COVID-19 patients by vaccine status.

Financial overview and grant giving statistics of Beth Israel Deaconess Hospital Milton Inc

Financial overview and grant giving statistics of Orphan Hospital Ward of Israel Inc.

IL：病床：每1000人在12-01-2012达3.300数量，相较于12-01-2011的3.370数量有所下降。IL：病床：每1000人数据按年更新，12-01-1960至12-01-2012期间平均值为6.140数量，共31份观测结果。该数据的历史最高值出现于12-01-1975，达8.079数量，而历史最低值则出现于12-01-2012，为3.300数量。CEIC提供的IL：病床：每1000人数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的以色列 – 表 IL.世行.WDI：卫生统计。

MIMIC-III is a large, freely-available database comprising deidentified health-related data associated with over forty thousand patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. The database includes information such as demographics, vital sign measurements made at the bedside (~1 data point per hour), laboratory test results, procedures, medications, caregiver notes, imaging reports, and mortality (including post-hospital discharge).MIMIC supports a diverse range of analytic studies spanning epidemiology, clinical decision-rule improvement, and electronic tool development. It is notable for three factors: it is freely available to researchers worldwide; it encompasses a diverse and very large population of ICU patients; and it contains highly granular data, including vital signs, laboratory results, and medications.

# ClinicalBERT - Bio + Clinical BERT Model

The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (cased_L-12_H-768_A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K + PMC 270K) & trained on either all MIMIC notes or only discharge summaries.

This model card describes the Bio+Clinical BERT model, which was initialized from BioBERT & trained on all MIMIC notes.

Pretraining Data

The Bio_ClinicalBERT model was trained on all notes from MIMIC III, a database containing electronic health records from ICU patients at the Beth Israel Hospital in Boston, MA. For more details on MIMIC, see here. All notes from the NOTEEVENTS table were included (~880M words).

Model Pretraining

Note Preprocessing

Each note in MIMIC was first split into sections using a rules-based section splitter (e.g. discharge summary notes were split into "History of Present Illness", "Family History", "Brief Hospital Course", etc. sections). Then each section was split into sentences using SciSpacy (en core sci md tokenizer).

Pretraining Hyperparameters

We used a batch size of 32, a maximum sequence length of 128, and a learning rate of 5 · 10−5 for pre-training our models. The models trained on all MIMIC notes were trained for 150,000 steps. The dup factor for duplicating input data with different masks was set to 5. All other default parameters were used (specifically, masked language model probability = 0.15 and max predictions per sequence = 20).

How to use the model

Load the model via the transformers library:

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
model = AutoModel.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")

More Information

Refer to the original paper, Publicly Available Clinical BERT Embeddings (NAACL Clinical NLP Workshop 2019) for additional details and performance on NLI and NER tasks.

Disease outcomes by SARS-CoV-2 IgG antibody levels among the antibody cohort.

Financial overview and grant giving statistics of Beth Israel Deaconess Hospital - Needham Inc.