2. Hospitals in detail

• Hospitals are service enterprises which provide healthcare in unity of place, time and action to personally attendant patients who often stay overnight.

• Hospitals are mainly focus on stationary care

• To maintain cost effectiveness and because of reform acts in health care they open up more and more to ambulant care as well.

• General hospitals

  • Average length of stay (LOS): mainly short LOS

• Spezialized hospitals

  • Average length of stay (LOS): mainly long LOS

  • specialities according to the type of illness (e.g. orthopedic clinic) or type of treatment (e.g. homeopathic clinic)

• depending on the number of beds

  very small hospitals	<= 50 beds
  small hospitals	51-200 beds
  medium hospitals	201-400 beds
  large hospitals	401-650 beds
  very large hospitals	>650 beds

  
  

• Physicians as employees of hospital (Anstaltskrankenhaus)

• Physicians as self-employed freelancers, i.e. general practitioner as head of a department/specialty (Belegkrankenhaus)

• Also: combination of both

• Classification according to scope (width) and depth of services

• Only used in some federal states (not any more in Baden-Württemberg) with partially different definitions

• Examples for classification criteria

  • Number of planned beds

  • Number, type and size of specialties

  • Personnel and equipment

  • Catchment area (hizmet alani, market)

• Mainly differentation between 4 levels:

  • Basic care (Grundversorgung)

  • Standard care (Regelversorgung)

  • Central care (Schwerpunktversorgung)

  • Maximal care (Maximalversorgung)

Public

• Regional authorities. e.g. the federation, federal state, region, country, community

• Mergers of regional authorities, e.g. working groups, special-purpose associations

• Social insurance carrier, e.g. state insurance agencies, labor unions

• Examples: Städtisches Klinikum Kalrsruhe, Stadtklinik Baden-Baden

Non-profit

• Free welfare work, parishes, foundations, associations

• Example: Diakonissen-Krankenhaus Karlsruhe

Private (for-profit)

• Business enterprise which is obliged to have a concession

  (ruhsat sahibi olmak zorunda olan ticari isletme)

• Example: Paracelsus-Klinik Karlsruhe, Rheumazentrum Baden-Baden

Acute care hospitals

• Inpatient or outpatient treatment of acutely ill patients

• Day and night receptiveness

receptiveness: Aufnahmebereitschaft

Long-term care hospitals

• Intensive medical treatment over a long period

Hospitals for chronically ill patients

• Intensive, long-term care

• Little medical attention necessary

• Smooth transition to intensive nursing homes

Legal frame: Hospital Financing Act (Krankenhausfinanzierungsgesetz, KGH)

• Obligation of federal states to create hospital and investment plans

  (running, daily costs -> insurance companies pay, building/maintaining etc. -> federal state)

  • determination of the number, locations, and specialties of hospitals as well as the number of beds, to guarantee a supply which meets the demand of the populaiton

  • Trade-off between efficiency and equity

Implementation and application of the KGH: State hospital laws (Landeskrankenhausgesetze, LKHG)

• Control element of federal states for regulation

• Right to be heard for hospitals who want to enter the state hospital plan and if necessary coordination with neighboring federal states

• Inclusion in the state hospital plan by an official notifcation of the state as basis for

  • Public funding for investements

  • Approval of hospital healthcare

    (public health insurances only allow (with few exceptions) treatment in hospitals in the state hospital plan for publicly insured patients)

  • Care rate agreements

Hospital Financing Act (KHG) (Krankenhausfinanzierungsgesetz)

Hospital Reimbursement Act (KHEntgG) (Krankenhausentgeltgesetz)

Social Security Statue Book V (Sozialgesetzbuch)

State Hospital Laws (Landeskrankenhausgesetze)

• Public authorities: investment costs -> investment backlog due to insufficient support

• Health insurances: operating costs

• Problem: restricted operational and economic autonomy

  -> no investments for rationalization (acquisition vs. operating costs)

Before 2004

• Lack of incentives for economic behavior due to cost covering principle

Since 2004

• Performance-based reimbursement system for all inpatient services, calculated by diagnosis related groups (DRG) (same for public and privately insured patients)

  -> sustainable improvement of quality, transparency and efficiency of inpatient services, e.g. by reducing the LOS

Exogenously given prices

• Hospitals cannot determine their own price structures

• Exceptions

  • Supplementary services, e.g. treatment by chief physician, single rooms

  • Discount and surcharges to take hospital-specific features into account

Economic classification system: grouping cases with similar average costs

Cost Weight (CW)

• Relative economic effort of patient treatment regarding a specific DRG in proportion to a “basic patient”

Case Mix (CM)

• Measure for composition of patient cases:

Case Mix Index (CMI)

• Average complexity level in a hospital

Determination of budgets:

Exemptions (muafiyet)

• Special cases that are not integrated in the DRG-System

• Re-admission into hospital (same DRG)

• Transfers

• Exceptional LOS (lower and upper trim point): day outliers

Link between revenues of hospitals and revenues of statutory health

insurances (principle of contribution rate stability, Beitragssatzstabilität)

=> Cost increases which exceed the changing rate of all contributions from health insured people have to be settled by the hospitals

Changing leadership behavior within hospitals

• From “reactive administration“ to “proactive management”

=> For example, case mix planning as a result

Hospital logistics describes the efficient planning, realization and control of patient-, material- and information-flow within a hospital as well as the patient´s stay in a hospital.

Hospital logistics includes both direct and indirect non-medical services

• direct: patient transportation

• indirect: storage and transport of medical goods

• capacity planning

• appointment planning

• transportation planning

• operating room planning

• nurse rostering

• layout planning

• iventory management

Objectives

• good medical care

• minimal resource-usage of non value-added activities, i.e., those that are irrelevant for the healing progress

• increasing customer satisfaction in particular by reducing waiting times

Requirements

• modern planning processes to meet the needs of multiple stakeholders (physicians, nurses, patients, visitors, hospital administration)

• Quick response to exceptional and unforeseeable incidents (“The emergency case is the normal case in a hospital”)

• Interception of capacity fluctuations and peak loads

Interception: önleme

Clinical pathways (critical paths, critical pathways or care paths) are management plans that display goals for patients and provide an optimal sequencing and timing of actions necessary to minimize delays and resource utilization and to maximize the quality of care, that is to achieve patient´s goals with optimal efficiency.

• increasing quality of care and treatment

• planning, standardization and optimization of medical care chains

• integration of all disciplines and occupational groups

• optimization and transparency of costs

• benchmarking

• improving documentation

• increase patient satisfaction and provide information for patients

1. Serial treatment

   • Patient is treated in different departments successively

   • e.g. first conservative treatment with medication , if there is no improvement: surgical intervention

1. 
   

2. Parallel treatment

   • A patient´s similar medical problems are treated parallel by two departments (one of them only supportive)

   • e.g. obtaining an expert opinion from another department

1. Team-treatment

   • Common treatment of a patient by several departments

   • e.g., cancer treatment by oncologists and internists

1. Cyclical treatment

   • cyclical/periodical treatment patterns

   • in particular (besonders) the treatment of chronic diseases, e.g.

   • diabetes patients: regular family doctor treatments (10 times a year), but less opthalmological examinations (1 time a year)

OR-planning: Operation room planning

much more uncertainty and dependencies

• Demand for medical services difficult to predict

• varying lengths of treatment from patient to patient

• integration of emergencies in the regular schedules and surgery plans

Hill-Burton-Formula (HBF)

Given:

Solution:

• Formula excludes providers´and customers´freedom of choice

• No consideration of seasonal variations and trends

• Rather arbitrary (keyfi) setting of an utilization level

• No consideration of parameters dynamics

  • e.g,. demand volume and LOS may change over time

• In some federal states expert´s reports have been ordered to develop alternative/supplemental methods for the determination of the necessary number of beds

• General characteristics of there reports

  • application of forecast methods

  • execution of experts interviews

  • consideration of economic efficiency

  • consideration of morbidity

morbidity:belirli bir bölgede hastalik sikligi

• How many patients to expect

  • Demography

  • Decisions of patients to go to a certain hospital

  • Morbidity

  • Medical advances

  • System changes (e.g., adoption of the DRGs)

• When to expect them

  • Seasonal variations

• How long they stay

  • Changes due to the reasons given for the first bullet point

  • Data on the legnth of stay distribution not only the average length of stay

we could use forecasting methods

• Judgmental methods: e.g., expert opinions are combined to estimate the influence of a certain new medical techniques

• Causal methods: e.g., a regression model is used to determine morbidities dependent on age, gender, etc.

• Time series: e.g., using historical data to the predict the demand volume next year

• analytical models: e.g., based on queueing theory

• Simulation

Typical time series patterns and forecasting models

Mean square error: ortalama karesel hata

Exponential Smoothing also called Exponential Moving Average

• considers all historical demands, but weights recent observations more than older ones

• Typical values for a are between 0.01 and 0.3

• Small a

  • Forecast responds to changes in demand slowly

  • Also older data relatively strong weighted (though never as much as more recent ones)

• Big a

  • Forecast responds to changes in demand fast

  • Information of historical data gets lost very fast

• Experimental determination of a good a-value for the previous example

• Similar to simple exponential smoothing, but with a smoothing factor for the y-intercept (a) and one for the slope (ß) of the forecasting function

• Forecasting formula

• Interpretation

• Focus on deciding on the number of beds for one hospital

• Using forecasting methods we can predict an average patient demand per year or month

• But due to uncertainty in demand we cannot predict the number of patients per day with precision

• To evaluate the decision on a certain number of beds we use simulation

  • We virtually test different capacity levels

  • We introduce stochastic demand and a length of stay distribution

  • We measure utilization and the number of rejected patients over a long time period