DE102019126461A1 - Method and system for processing optimized parameters - Google Patents

Abstract

Method (300) for processing optimized parameters for at least one medical device in an operating room in order to provide a medical treatment of a patient depending on a type of treatment to which the treatment is assigned and a patient target group to which the patient is assigned, with the best possible To achieve a patient result as a result of the treatment for the patient, the method with the steps: acquiring (302) inventory data, analyzing (304) the inventory data in order to determine target parameters, storing (306) the target parameters, and reproducing and / or applying (308) the stored target parameters. A corresponding system (2) is also shown.

Description

The invention relates to a method for processing optimized parameters for at least one medical device in an operating room, as well as a corresponding system.

An operating room (OR), in particular a so-called integrated operating room, has a large number of connected medical devices that can usually also be controlled remotely. However, it requires many years of experience in order to make the settings in the best possible way.

It is therefore an object of the present invention to provide an improved method and a corresponding system in which optimized parameters can be processed.

• - Erfassen von Bestandsdaten, die von einer Mehrzahl von vergangenen Behandlungen jeweils die Ist-Parameter des mindestens einen medizinischen Geräts, das während der medizinischen Behandlung im Operationssaal verwendet wurde, die Behandlungsart, die Patientenzielgruppe und das Patientenergebnis aufweisen,
• - Analysieren der Bestandsdaten, um Soll-Parameter für das mindestens eine medizinische Gerät zu ermitteln, die für eine bestimmte Behandlungsart und/oder eine bestimmte Patientenzielgruppe ein optimiertes Patientenergebnis erwarten lassen,
• - Speichern der Soll-Parameter, und
• - Wiedergeben und/oder Anwenden der gespeicherten Soll-Parameter zum Einstellen des mindestens einen medizinischen Geräts während einer aktuellen Behandlung einer bestimmten Behandlungsart und/oder einem Patienten einer bestimmten Patientenzielgruppe.

According to a first aspect, the object is achieved by a method for processing optimized parameters for at least one medical device in an operating theater in order to provide medical treatment of a patient depending on a type of treatment to which the treatment is assigned and a patient target group, the patient is assigned to achieve the best possible patient result as a result of the treatment for the patient, the method with the steps:

• - Acquisition of inventory data, which each have the actual parameters of the at least one medical device that was used during the medical treatment in the operating room, the type of treatment, the patient target group and the patient result from a plurality of past treatments,
• - Analyzing the inventory data in order to determine target parameters for the at least one medical device, which allow an optimized patient result to be expected for a specific type of treatment and / or a specific patient target group,
• - Saving the target parameters, and
• - Playback and / or use of the stored target parameters for setting the at least one medical device during a current treatment of a specific type of treatment and / or a patient of a specific patient target group.

The claimed process therefore works in several stages. So there is the recurring step of collecting inventory data. During or after a treatment, the actual parameters of at least one medical device, preferably of a large number or of all medical devices, are recorded that were used during the medical treatment in the operating room. The type of treatment, the patient target group and the patient result are recorded as additional information.

In a further recurring step, the inventory data are analyzed in order to determine target parameters for the at least one medical device. The target parameters are determined as a function of the type of treatment and / or the patient target group in order to be able to respond more precisely to specific treatment situations. Since the patient results for certain actual parameters or arrays of actual parameters are known, it can be determined for which target parameters an optimized patient result can be expected. These target parameters are saved.

If another operation is pending, on the one hand there is the possibility of reproducing the stored target parameters for a certain type of treatment and / or a certain patient. The doctor or the surgical team can then decide whether the proposed target parameters should actually be used or whether they should still be adjusted. Another possibility is to use the stored target parameters immediately for the current treatment. This reduces the risk of errors when entering or manually accepting parameter settings.

This completely solves the problem.

In a preferred embodiment, the actual parameters of the at least one medical device have a course of a parameter over time.

This refinement is advantageous because optimized dynamic parameter settings can also be processed in this way, i.e. parameter settings that are varied over the course of a treatment, possibly also depending on a specific phase of the operation.

wobei ein Wichtungsfaktor und PP ein Patientenparameter ist.In a further preferred embodiment, the patient result is calculated as $P\mathrm{E.}={\displaystyle {\sum }_{k=1}^N{\alpha }_{k}P{P}_k},$

where a weighting factor and PP is a patient parameter.

Such a design is transparently understandable.

In a further preferred embodiment, the patient result is determined taking into account at least one element individually assigned to the patient, in particular a patient parameter selected from the group consisting of diagnosis, age, weight, body size, gender, place of residence, medication, existing diseases, risk factors, duration of the Stay, number of complications, re-admission, survival rate and number of follow-up treatments.

This configuration enables individual factors to be taken into account particularly well in order to achieve the most meaningful patient result possible.

In a further preferred embodiment, the patient result is determined repeatedly over the course of time.

This embodiment is advantageous because, on the one hand, a patient result can be determined in the short term, but in addition a long-term patient result can also be determined, which is generally more meaningful. For example, it can be recorded that a patient result was good immediately after the operation, but the patient result is worse due to later complications and multiple follow-up treatments.

In a further preferred embodiment, the inventory data is recorded over several operating theaters and / or over several hospitals.

This refinement is advantageous because in this way a larger database is created with which optimized target parameters can be determined more quickly and more precisely.

In a further preferred embodiment, the inventory data also have an assessment by personnel who were involved in the medical treatment in the operating room.

This embodiment is advantageous because, in addition to the objectively measurable data, subjective evaluations can now also be taken into account. It is quite possible that the objective data do not or only insufficiently record certain events, which, however, play a significant subjective role for humans.

In a further preferred embodiment, the inventory data also include a consumption of at least one consumable material while the medical treatment is being carried out in the operating room.

This refinement is advantageous since the consumption of consumables can also provide a conclusion as to the suitability or unsuitability of a certain type of treatment. This embodiment ensures that the patient result is assigned to actual parameters in a predetermined manner. This improves comparability.

In a further preferred embodiment, the patient result is assigned to actual parameters that are stored automatically or at predetermined times or in the case of predetermined events.

This configuration ensures that data are continuously collected that document the relationship between the patient result and the actual parameters associated with it. These data enable a good determination of the target parameters.

In a further preferred embodiment, the method also has the step of sharing target parameters with at least one further operating room.

This embodiment is advantageous because optimized target parameters can also be made available to other operating theaters or hospitals. This is particularly advantageous when a large number of certain types of treatment are carried out in one operating room and those in another Operating theater are rarely performed. In this way, the data from the other operating room can be used in the latter operating room.

In a further preferred embodiment, the inventory data also has personal information that identifies the staff who were involved in the medical treatment in the operating room.

This configuration offers more extensive evaluation options, since the people involved in an operation also have a considerable influence on the result of the operation.

In a further preferred embodiment, the personal information is evaluated together with the patient result in order to determine a medical expert and / or an optimized composition of a team for a specific type of treatment and / or a specific patient target group.

This refinement is advantageous because in this way a database is created in order to further improve the patient result in the medium term.

In a further preferred embodiment, the analysis of the inventory data takes place using at least one statistical analysis.

This refinement is advantageous because in this way a tried and tested and mathematically secured path is followed in order to ensure a reliable and secure analysis of the inventory data.

In a further preferred embodiment, the rendering includes the presentation of a list in which actual parameters and / or target parameters are contained in connection with the respective patient result, the list being sorted in particular according to the patient result or an index value.

This refinement is advantageous since it is made easy for a user in this way to obtain the data relevant to him. As a rule, the user will be interested in seeing actual parameters and / or target parameters in connection with the best possible patient result. It is also possible to sort according to an index value which, in addition to the patient result, also takes into account other factors, in particular subjective factors. In this way, the parameters for the at least one medical device can be determined quickly even in an emergency.

In a further preferred embodiment, an automatic or manual evaluation of the patient result takes place after various defined time segments.

This configuration ensures that data are continuously collected that document the relationship between the patient result and the actual parameters associated with it. These data enable a good determination of the target parameters.

According to a further aspect, the object is achieved by a system for processing optimized parameters for at least one medical device in an operating room, the system with at least one data processing unit and at least one memory, wherein the at least one memory contains instructions that cause when they are from the at least one data processing unit can be carried out so that the at least one data processing unit carries out a method according to one of the preceding claims.

In the following, further advantageous embodiments are disclosed which can be used both in the method and in the system alone or in combinations or in sub-combinations.

In one embodiment, the list can be sorted according to an index value I _i , which results from I _i = BW _i .

ergibt, wobei α₁ und α₂ globale Gewichtungsfaktoren für die Bewertung und Verbrauchmaterial sind und α_3,j zusätzliche Gewichtungsfaktoren für die einzelnen Verbrauchsmaterialien (j = V₁...V_M) sind.In a further embodiment, the list can be sorted according to an index value I _i , which is derived from $${\mathrm{I.}}_i={\alpha }_1\mathrm{B.}{\mathrm{W.}}_i+{\alpha }_2{\displaystyle \sum _{j=1}^{\mathrm{<figure-callout\; id="3"\; label="M.\; \alpha "\; filenames="DE102019126461A1\_0005.png"\; state="\{\{state\}\}">M.</figure-callout>}}{\alpha }_{}{}_{\mathrm{3,}j}\frac{\left|V_{i,j}-{\overline{V}}_j\right|}{\text{Max}\left(V_j\right)-\text{min}\left(V_j\right)}}$$

results, where α ₁ and α _{2 are} global weighting factors for the evaluation and consumables and α _{3, j are} additional weighting factors for the individual consumables (j = V ₁ ... V _M ).

ergibt, wobei α₁, α₂ und α₄ globale Gewichtungsfaktoren für die Bewertung, das Verbrauchmaterial und die Patienteninformationen sind, α_3,j und α_5,k zusätzliche Gewichtungsfaktoren für die einzelnen Verbrauchsmaterialien (j = V₁...V_M) und Patienteninformationen (k = P₁...P_N) sind und Pk die Angaben für den geplanten Patienten sind. Für fehlende Informationen bzw. nicht definierte Angaben von Ṗ_k wird der entsprechende lokale Gewichtungsfaktor auf α_5,k = 0 gesetzt.In a further embodiment, the list can be sorted according to an index value I _i , which is derived from $${\mathrm{I.}}_i={\alpha }_1\mathrm{B.}{\mathrm{W.}}_i+{\alpha }_2{\displaystyle \sum _{j=1}^{\mathrm{<figure-callout\; id="3"\; label="M.\; \alpha "\; filenames="DE102019126461A1\_0005.png"\; state="\{\{state\}\}">M.</figure-callout>}}{\alpha }_{}{}_{\mathrm{3,}j}\frac{\left|V_{i,j}-{\overline{V}}_j\right|}{\text{Max}\left(V_j\right)-\text{min}\left(V_j\right)}}+{\alpha }_{\mathrm{4th}}{\displaystyle \sum _{k=1}^N{\alpha }_{\mathrm{5,}k}\frac{\left|P_{i,k}-{\dot{P}}_k\right|}{\text{Max}\left(P_k\right)-\text{min}\left(P_k\right)}}$$

results, where α ₁ , α ₂ and α _{4 are} global weighting factors for the assessment, the consumables and the patient information, α _{3, j} and α _{5, k} additional weighting factors for the individual consumables (j = V ₁ ... V _M ) and are patient information (k = P ₁ ... P _N ) and Pk is the information for the planned patient. For missing information or undefined details of Ṗ _k , the corresponding local weighting factor is set to α _{5, k} = 0.

Further advantageous options for using the method and the system, as well as details of the use, are explained below. In the following, the abbreviation OP is used for operation.

An exemplary, simplified procedure, also called a workflow, has the following steps: a) the patient is admitted to the hospital; b) Diagnosis is made and surgery planning is initiated; c) The OR planning defines, among other things, the team and the required instruments, devices and consumables; d) The surgical intervention is roughly divided into peri, intra and postoperative phase, namely d1) in the perioperative phase, among other things, the devices are switched on and initial values are set for the device parameters, e.g. insufflator pressure 15mmHg, d2) in the intraoperative phase the device settings are checked again and again and changed if necessary, eg the insufflator pressure is increased to 20mmHg or decreased to 10mmHg, d3) in the postoperative phase, among other things, the devices are switched off; e) patient is recovering; and f) patient is discharged from hospital

The system, in particular a controlling system part, also called a controller, suggests optimal values for the device parameters to the OR team in the perioperative phase based on the type of intervention and patient information (age, gender, BMI, medical history).

The system, especially the controller, continuously suggests optimal device parameter values to the OR team during the intraoperative phase, based on progress, progress and time.

The system, in particular an observing system part, also called observer here, advantageously records the device parameters during the entire OP and uses this information to calculate new, optimal device parameter values. The calculation takes place in the system, in particular by means of a processing system part, also referred to here as a processor. In addition, information on consumption is advantageously recorded at the end (e.g. gas, rinsing liquid, etc.) in order to calculate optimizations for the course of the operation, which in turn can be incorporated into the planning and perioperative phase as typical consumption values.

A large number of surgical devices, in which a large number of parameters can be set, are often used to carry out an operation. The proposed system advantageously links parameter settings with measurable variables such as the type of intervention, patient information, consumables and a corresponding evaluation by the surgeon or surgical team with regard to the implementation of the surgery. Advantageously, optimal settings are then determined by means of algorithmic methods or algorithms and proposed to the surgeon or surgical team based on the measurable variables (parameters).

• Verschlussgeschwindigkeit (Shutter), Helligkeit (Brightness), Vergrößerung (Enhancement); b) Kaltlichtquelle: Intensität, Filter; c) Insufflator: Druck, Durchflussmenge, Temperatur; d) Pumpe: Pumpdruck, Pumpfluss, Saugdruck; e) HF-Vorrichtung: Schneideffekt (Cut-Effect), Schneidart (Cut-Mode), Schneidleistung (Cut-Power), Koaguliereffekt (Coag-Effect), Koagulierart (Coag-Mode), Koagulierleistung (Coag-Power); f) Motorsystem: Modus, Geschwindigkeitsbegrenzung, Kühlung (Pumpeneinstellungen)

Typical surgical devices are: camera, cold light source, insufflator, pump, HF device, motor system. These devices offer, among other things, the setting of the following parameters: a) Camera:

• Shutter speed (Shutter), brightness (Brightness), magnification (Enhancement); b) cold light source: intensity, filter; c) Insufflator: pressure, flow rate, temperature; d) Pump: pump pressure, pump flow, suction pressure; e) HF device: cutting effect (cut effect), cutting type (cut mode), cutting performance (cut power), coagulation effect (coag effect), coagulation type (coag mode), coagulation performance (coag power); f) Motor system: mode, speed limit, cooling (pump settings)

These parameters are set by the surgeon or surgical team for the respective needs. This results in one or more parameter configurations over the period of the surgical intervention, which map the respective settings of the devices for certain periods of time during the surgical intervention. Configurations can, but do not have to, correlate with certain phases in the surgical procedure.

It is possible that a configuration remains the same over the entire period of the OP, i.e. the settings of the devices or the device parameters remain constant during the intervention, or that the configuration changes at least once or several times during the OP. In addition, the configurations can be based on the phases, i.e. the settings of the devices or the device parameters change with each phase.

The settings of the devices are continuously recorded via the system, in particular the observer. This is done via the connection using proprietary device interfaces such as KARL STORZ Communication Bus (SCB) or open device interfaces such as Open Surgical Communication Protocol (OSCP) or Integrating the Healthcare Enterprise (IHE) Patient Care Device (PCD).

In addition, the system, in particular the observer, records the operation, patient-specific information (e.g. age, weight, height) and consumables (e.g. insufflation gas, irrigation fluids). Surgical interventions and patient-specific information are recorded e.g. using HL7 messages, consumables using proprietary or open device interfaces.

A specific data set is thus created for each surgical intervention, consisting of surgical intervention (type), patient-specific information (patient characteristics), device configurations and the assessment by the surgeon or the surgical team. The evaluation takes place with regard to the implementation taking into account the course of the operation and the materials used, e.g. evaluation on a scale from 1 to 10 with 1 = good and 10 = bad.

As part of an initial acquisition phase, a database with specific data records is set up in the system, in particular in the processor.

These data records are managed in the system, especially in the processor, and made available via the controller for future OP interventions. The surgeon or the OR team can have device configurations displayed, e.g. sorted by rating, before the start of an operation. The display can be restricted or evaluated by including additional information such as surgical intervention, patient information, etc. in order to propose an optimal (initial) device configuration for this surgical intervention and patient characteristics. Further optimal device configurations are suggested according to the temporal course of the surgical intervention (e.g. according to phases).

The evaluation and / or sorting of the data records can take place on the basis of the index value explained above, one or more of the following determinations advantageously being selected: a) determination exclusively after evaluation; b) Determination after evaluation and consumables; and c) investigation based on assessment, consumables, and patient information.

As a rule, data records are then sorted based on the index value and proposed to the surgeon or OR team. The proposal is advantageously shown on a display. In addition, after confirmation, device configurations can advantageously be transferred to or adopted from the devices.

The indication of the planned intervention can advantageously be used to filter the sorted results. This is explained using the OPS code as an example. The operation and procedure key (OPS) is the official classification for encrypting operations, procedures and general medical measures in inpatient and outpatient operations.

The OPS code is a sequence of characters that increases in precision as the sequence progresses. For example, a "5-""Operations", a "5-511""Cholecystectomies" and a "5-511.11" indicate the procedure "Cholecystectomy: simple, laparoscopic: without laparoscopic revision of the bile ducts".

The indication of the planned surgery, e.g. "lapa" is used as a filter - only lines in which "lapa" occurs are displayed. The surgeon or the OR team decide on the order and display of optimal device configurations by specifying the planned OP, patient information of the planned patient and the corresponding global and / or local weighting factors.

Every further surgical intervention is recorded by the system, advantageously continuously, and added to the existing database. This therefore leads to a continuous increase in the database (data records). Existing data records can be edited, corrected and / or deleted in the system, especially in the processor.

The system can advantageously be connected to other systems in order to acquire a larger number of data records and to use them to determine the optimal device configuration. This can e.g. be several operating theaters within a hospital or the connection of several hospitals. The system, in particular the processor, advantageously transmits data records to a higher-level system and can (on request) receive back optimal data records. In addition, the global and / or local weighting factors are advantageously transmitted and exchanged.

Furthermore, the system advantageously analyzes recorded and evaluated data records. With the help of a principal component analysis, the relationship between individual variables (parameters) is determined and the relationship between surgical intervention, patient characteristics, consumables and device parameters is determined.

In the system and method described here for optimizing surgical treatment settings (OP configurations) on the basis of patient findings data, a continuous refinement loop is advantageously used, ie data is continuously collected and used to review and improve the optimal treatment settings over time on the basis of Measures used for a patient outcome (“patient outcome”), the patient outcome being a numerical evaluation of the treatment of the patient.

Surgical parameter values, i.e. parameter values that characterize the condition of medical devices, including complete operating theater configurations that are used in surgery, are recorded and combined with the result of the medical patient result. This information can be collected across multiple operating rooms and multiple hospitals for multiple treatments with a large number of patients for a specific patient target group.

The collected information about the treatment settings (OR configurations) is continuously analyzed and processed in order to obtain optimal OR configurations for future treatments. Based on certain patient information such as diagnosis, case (surgical procedure), age, weight, height, gender, medication, other diseases and patient-specific risk factors, optimal treatment parameters (operating theater configurations) are selected in order to achieve the best surgical result. All operations, their settings and states as well as the initial state of the patient, the doctor and the team constellation are collected and correlated with the patient result.

Optimal treatment settings (OR configurations) are provided by the system, can be checked by the OR team and, after approval, are used to configure devices and complete OR environments.

The system and method can be used to provide optimal treatment settings (OP configurations): a) initially, i.e. at the beginning, give the surgeon the opportunity to select a treatment as a starting point, i.e. to improve patient target group-specific treatment settings (OP configurations); b) continuous, i.e. the treatment is monitored by the observer and the treatment settings (operating theater configurations) are continuously recorded over all phases of the medical treatment.

The system and method are also used to: a) define optimal medico-surgical treatment options for specific target groups; b) find clinical experts; and / or c) to find optimal team constellations and conditions in the operating room.

The system is advantageously designed as a cascaded architecture that ranges from a single operating room to a combination of several operating rooms in a hospital. In addition, the system can be expanded to include multiple hospitals. For example, cloud-based storage could advantageously be used for exchanges between hospitals.

An important aspect of this invention is the correlation of treatment settings (OP configurations) with the patient result in order to identify optimal treatment settings (OP configurations) for “specific medical interventions” and “specific patient target groups”.

As the state of the art with regard to the patient outcome, it was found that there is a lot of research literature, patents and patent applications for quantifying the patient outcome in this area, but with a focus outside the operating room. It is a special feature of this patent application that the patient result is used for feedback in order to evaluate specific medical treatment settings (operating theater configurations). Linking treatment settings (operating theater configurations) with the patient result also has the advantage that individual hospitals can determine the patient result individually according to their own criteria.

It goes without saying that the features mentioned above and those yet to be explained below can be used not only in the respectively specified combination, but also in other combinations or on their own, without departing from the scope of the present invention.

• 1 ein Ausführungsbeispiel eines Systems zur Verarbeitung von optimierten Parametern für mindestens ein medizinisches Gerät in einem Operationssaal;
• 2 eine erste Ausführungsform einer Tabelle für Behandlungseinstellungen;
• 3 eine zweite Ausführungsform einer Tabelle für Behandlungseinstellungen;
• 4 eine dritte Ausführungsform einer Tabelle für Behandlungseinstellungen;
• 5 eine Ausführungsform einer Tabelle zur Darstellung von Patientenergebnissen: und
• 6 ein Ausführungsbeispiel eines Verfahrens zur Verarbeitung von optimierten Parametern

Exemplary embodiments of the invention are shown in more detail in the drawing and are explained in more detail in the description below. Show it:

• 1 an embodiment of a system for processing optimized parameters for at least one medical device in an operating room;
• 2 a first embodiment of a treatment setting table;
• 3 a second embodiment of a treatment setting table;
• 4th a third embodiment of a treatment setting table;
• 5 an embodiment of a table for displaying patient results: and
• 6th an embodiment of a method for processing optimized parameters

In 1 is a system for treatment optimization in an operating room 1 shown in the form of a block diagram and with the reference number 2 marked. The system 2 has an observation unit 32 , identified as "Observer", a process processing unit 34 , labeled as "Processor", and a control unit 36 marked as "Controller". The observation unit 32 and the process processing unit 34 are via a bus connection 33 , for example an Ethernet bus connection. The process processing unit 34 and the control unit 36 were via a bus connection 35 , for example an Ethernet bus connection. The process processing unit 34 can also be used as a general data processing unit 34 be understood.

The observation unit 32 serves as an observer for treatment settings (operating theater configurations) of medical and non-medical devices. In the figure, medical devices are denoted by the reference number 20th marked. An insufflator 24 and an engine system 26th are shown as an example in the figure. However, this exemplary listing of two medical devices is not to be understood as restrictive, as the additional medical devices k and I show. In the figure, non-medical products are denoted by 10 marked. A hospital information system (HIS) 14th and a picture archiving and communication system (PACS) 16 are shown by way of example in the figure. However, this exemplary listing of two non-medical devices (IT systems) is not to be understood as restrictive, as the additional non-medical devices n and m show.

The communication between the medical devices 20th and the observation unit 32 takes place via a bus system 21 . Communication between the non-medical devices 10 and the observation unit 32 takes place via a bus system 11 .

One or more medical devices within an operating room 1 (here in particular an integrated operating room) can advantageously use one or more medical networks 22nd with one or more observation units 32 be connected. While the system with medical equipment 20th was illustrated being attached to a single medical network 22nd (MD network) are connected, the system can handle many different medical networks 22nd record those at one or more observation units 32 are connected. The MD network 22nd can use RS232 or fieldbus (e.g. KARL STORZ Communication Bus (SCB)) or Ethernet or other standards for communication between devices, e.g. wireless transmission.

One or more IT systems within an operating room, in particular an integrated operating room or hospital, can advantageously be connected to one or more observation units via one or more IT networks 12th be connected. While the system was illustrated with IT systems attached to a single IT network 12th connected, the system can handle many different IT networks 12th that are connected to one or more observation units. The IT network 12th can use ethernet or other communication standards. The IT network 12th is about a connection 41 with the process processing unit 34 and over a connection 43 with the control unit 36 connected.

The process processing unit 34 is used to process treatment settings (OR configurations) of medical and non-medical devices as well as to calculate and define treatment settings in connection with the patient result.

The control unit 36 serves as a control unit to inform the OR team about the optimal treatment settings (OR configurations) and the possibility of controlling medical devices after approval by the OR team.

One or more controllers can be connected to medical devices in an operating room via one or more medical networks. While the system was illustrated with medical devices connected to a single medical network 22nd (MD network) are connected, the system can handle many different medical networks 22nd that are connected to one or more controllers. The system contains at least one data store 37 . The data, which each have the actual parameters of the at least one medical device that was used during the medical treatment in the operating room, the type of treatment, the patient target group and the patient result from a plurality of past treatments, are referred to as inventory data.

The observation unit 32 , the process processing unit 34 and the control unit 36 form a closed circuit, ie treatment settings (OP configurations) from the control unit 36 are then from the observation unit 32 observed and thus used in a continuous refinement.

The observation unit 32 , the process processing unit 34 and the control unit 36 are connected to peripheral devices (devices). An example of a setting with typical devices such as the touch-sensitive monitor (touch screen) 32a , a keyboard 32b and a mouse 32c is shown.

The system is advantageously designed as a cascading architecture that of a single operating room 1 from the connection of several operating theaters in a hospital to the further connection of several hospitals to a cloud. An example of a representation in which an observation unit 32 Information for a single operating room 1 who collects with other observation units 132 , 232 within the same or other hospitals is in 1 shown. The observation units 32 , 132 , 232 are via a bus connection 45 , for example an Ethernet bus connection. Further bus connections are denoted by 41, 43.

The information gathered can be used by the supreme observation unit 232 are made available and can then be used by the process processing units 34 , 134 , 234 used to define optimal treatment settings for multiple operating rooms, hospitals and / or countries. The process processing units 34 , 134 , 234 are via a bus connection 47 , for example an Ethernet bus connection.

At each level, optimal treatment settings can be derived and then passed on to the surgeon, the surgical team, etc. using the control unit 36 be returned. So will the surgeon, the Surgical team etc. offered to choose the optimal treatment settings based on a local or global assessment.

The system can be designed as a service-oriented architecture. At least one cloud server or data store 137 , 237 can be coupled to the uppermost observation unit and / or process processing unit.

The top process processing unit has access to the one or more observation units in order to edit and define the treatment settings for the patient result via one or more operating theaters.

The one or more observation units 32 collect information about the treatment settings, that is, it is parameter values of all medical and non-medical devices. The information on the treatment settings can be patient-dependent and patient-independent data. The information about the treatment settings of medical devices can be collected or continuously collected at individual points in time such as the beginning or end or at certain phases during a treatment.

The one or more observation units also record context-related treatment settings (OP configurations) that relate to the patient result. This information can be provided from the at least one operating room or the IT systems such as HIS, PACS, PDMS (patient data management system), etc. Context-relevant information is used to correlate patient-independent data with a patient and / or a treatment. Context-relevant information can also be monitored and updated multiple times after a treatment to keep patient results up to date.

The surgeon, the OR team, etc. can use the observation unit 32 about peripherals like 32a , 32b , 32c interact, for example to define a selection of important and / or valuable device parameters, collection times, etc. The information on the treatment settings is continuously collected and stored.

Treatment settings (OR configurations), information and context-related information can be stored as data in NoSQL databases with JSON notation. 2 Figure 3 shows a first embodiment of a table. The assignment of information about the treatment settings to a treatment can be saved. 3 Figure 12 shows a second embodiment of a treatment setting table.

The information on the treatment settings can be enriched with additional information such as phases and / or states of a treatment. 4th shows a third embodiment of a table which defines a phase for certain periods of time. Phases and / or states can be defined manually and / or calculated automatically.

The process processing unit 34 has access to one or more observation units 32 to process and define treatment settings based on patient outcome. The patient result is a parameter that can be defined manually or calculated automatically. 5 shows, as an example, an embodiment of a table for its representation.

Generally speaking, and not just related to the present embodiment, it is an important aspect of this invention to provide a system and method for continuously improving treatment settings in order to optimize patient surgical outcome for modern operating rooms, particularly integrated operating rooms (IOR). The assessment and evaluation of treatment settings (OP configurations), ie the correlation of certain treatment settings with the patient result, whereby the patient result can be a single parameter, can be defined manually by a surgeon, a surgical team and / or specially trained persons or by the system can be calculated partially or fully automatically using patient-related information from HIS components. The literature offers many solutions for deriving patient outcomes. In one embodiment of the present invention, a surgeon or an operating room team can define the patient outcome for certain treatment settings, i.e. information about the treatment settings, through the use of a human machine interface (HMI).

In an advantageous embodiment, a surgeon can manually define the patient result and the treatment settings on the process processing unit 34 with the peripheral devices (apparatus) 32a , 32b and or 32c evaluate. In a further advantageous embodiment, the patient result can be calculated with the aid of a linear regression, as described above. Weight factors can be defined by the guidelines of the surgeon, the operating theater team or the hospital, with the patient parameters either via the observation unit 32 and / or the IT network components 12th to be provided.

Newly observed treatment attitudes are used to continuously or periodically refine and review the correlation between treatment attitudes and patient outcome. The refinement can be done manually by the surgeon, the OR team, etc., or it can be calculated (processed) automatically.

Optimal treatment settings (OP configurations) can be obtained from the system prior to the operation (treatment) on the basis of characteristic patient parameters and made available to the surgeon, the OR team, etc. via the control.

The one or more control units 36 provide optimal treatment settings based on patient characteristics such as diagnosis, case (surgical procedure), age, weight, gender, medication. Optimal treatment settings can be reviewed by the OR team and used for device configuration after approval. The interaction with the control unit can be done via the peripheral devices (apparatus) 32a , 32b and or 32c respectively.

The system and process described below are designed as a continuous refinement cycle, i.e. data is continuously collected and used to review and improve the optimal treatment settings over time based on patient outcomes.

During the operation, information about the treatment settings (OR configurations) is collected. Optimal treatment settings (OP configurations) can be calculated and provided at several points in time: a) at the beginning, i.e. at the beginning, the surgeon is given the opportunity to select a treatment as a starting point, i.e. to improve the patient-target group-specific treatment settings; b) continuously, i.e. the treatment is monitored by the observer and the device parameters are continuously recorded over all phases of the medical treatment; c) at all stages of the operation; d) at the end of the operation; and e) each time the patient outcome is updated.

Thus, an advantageous method for processing optimized parameters for at least one medical device in an operating room and a corresponding system for processing optimized parameters for at least one medical device in an operating room have been described.

Claims (16)

Method (300) for processing optimized parameters for at least one medical device in an operating room in order to provide a medical treatment of a patient depending on a type of treatment to which the treatment is assigned and a patient target group to which the patient is assigned, with the best possible quality To achieve patient outcome as a result of treatment for the patient, the procedure with the steps: - Acquisition (302) of inventory data which each have the actual parameters of the at least one medical device that was used during the medical treatment in the operating room, the type of treatment, the patient target group and the patient result from a plurality of past treatments, - Analyzing (304) the inventory data in order to determine target parameters for the at least one medical device, which allow an optimized patient result to be expected for a specific type of treatment and / or a specific patient target group, - Save (306) the target parameters, and - Reproduction and / or application (308) of the stored target parameters for setting the at least one medical device during a current treatment of a specific type of treatment and / or a patient of a specific patient target group. Procedure according to Claim 1 , wherein the actual parameters of the at least one medical device have a course of a parameter over time. The method according to any one of the preceding claims, wherein the patient result is calculated as $P\mathrm{E.}={\displaystyle {\sum }_{k=1}^N{\alpha }_{k}P{P}_k},$

where a weighting factor and PP is a patient parameter. Method according to one of the preceding claims, wherein the patient result is determined taking into account at least one element individually assigned to the patient, in particular a patient parameter selected from the group consisting of diagnosis, age, weight, body size, gender, place of residence, medication, existing diseases, risk factors , Length of stay, number of complications, re-admission, survival rate and number of follow-up treatments. Method according to one of the preceding claims, wherein the patient result is determined repeatedly over the course of time. Method according to one of the preceding claims, wherein the inventory data are recorded over a number of operating theaters and / or over a number of hospitals. Method according to one of the preceding claims, wherein the inventory data further comprises an assessment by personnel who were involved in the medical treatment in the operating room. Method according to one of the preceding claims, wherein the inventory data furthermore includes a consumption of at least one consumable material while the medical treatment is being carried out in the operating room. Method according to one of the preceding claims, wherein the patient result is assigned to actual parameters stored automatically or at predetermined times or in the case of predetermined events. Method according to one of the preceding claims, further comprising the step of sharing target parameters with at least one further operating room. A method according to any one of the preceding claims, wherein the inventory data further comprises personnel information that identifies the personnel who were involved in the medical treatment in the operating room. Procedure according to Claim 11 , wherein the personal information is evaluated together with the patient result in order to determine a medical expert and / or an optimized composition of a team for a certain type of treatment and / or a certain patient target group. Method according to one of the preceding claims, wherein the analysis of the inventory data takes place using at least one statistical analysis. Method according to one of the preceding claims, wherein the rendering comprises the presentation of a list in which actual parameters and / or target parameters are contained in connection with the respective patient result, the list being sorted in particular according to the patient result or an index value. Method according to one of the preceding claims, wherein an automatic or manual evaluation of the patient result takes place after different fixed time periods. System (2) for processing optimized parameters for at least one medical device (24) in an operating room (1), the system (2) with at least one data processing unit (34) and at least one memory (37), the at least one memory ( 37) contains instructions which, when executed by the at least one data processing unit (34), cause the at least one data processing unit (34) to carry out a method according to one of the preceding claims.

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