Chronic pain patient identification system

• US 20020128867A1
• Filed: 04/27/2001
• Published: 09/12/2002
• Est. Priority Date: 12/29/2000
• Status: Abandoned Application

• Current Assignee: Medtronic Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Incorporated (Medtronic Plc)
• Inventors: Goetzke, Gary A., Carlson, Angeline M., Johns, Thomas N.P., Reid, Malcolm E., Borg, John W.
• Application Number:
  US09/844,195
• Publication Number:
  US 20020128867A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: A61B 5/4824 Touch or pain perception evaluation A61B5/4821 takes precedence
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

Potential chronic pain patients are identified in a population such as an employer or medical care payer database using a method or computer software product to improve accuracy in identifying potential chronic pain patients, decrease the time required to identify potential chronic pain patient increasing opportunities for early intervention, identify selected potential chronic pain patients based upon preference of stakeholders, and many other benefits. Desired patient indicia including direct medical indicia, indirect medical indicia, and non-medical indicia are selected to serve as independent variables. At least one chronic pain indication is selected to serve as a dependent variable. A chronic pain model is created using the patient indicia and the chronic pain indication. The chronic pain model is applied to the population and potential chronic pain patients are identified by selecting individuals from the population that conform to the chronic pain model. Many different embodiments of the chronic pain patient identification system method and software product are possible.

Claims

1. A method for identifying individuals at risk for chronic pain condition in a population, comprising:

selecting direct medical indicia associated with chronic pain that serve as independent variables;

selecting indirect medical indicia associated with chronic pain that serve as independent variables;

selecting non-medical indicia associated with chronic pain that serve as independent variables;

selecting a chronic pain indication that serves as a dependent variable;

creating a chronic pain model using direct medical indicia, indirect medical indicia, non-medical indicia, and chronic pain indication;

applying the chronic pain model to a population to create a patient mathematical expression for each member of the population; and

,

identifying potential chronic pain patients by comparing each patient mathematical expression to selection objectives.

2. The method as in claim 1 wherein the chronic pain model comprises

a logic structure to define a logical decision process to operate on the independent variables and to progressively reach greater certainty about potential chronic pain patients;

weighted variables to reflect greater relevance of certain direct medical indicia, indirect medical indicia, and non-medical indicia to the chronic pain indication; and

,

equations that represent relationships between or among weighted variables to form a chronic pain inference engine.

3. The method as in claim 2 wherein the chronic pain inference engine comprises,

at least fifty dependent variables;

at least thirty independent variables; and

,

at least fifty equations.

4. The method as in claim 2 wherein the logic structure is developed using Chi-Square Automatic Interaction Detection (CHAID) analysis to establish relationships between a dependent variable and independent variables.

5. The method as in claim 2 wherein the logic structure is developed using Classification Adjusted Regression Tree (CART) analysis to establish relationships between the dependent variable and the independent variables.

6. The method as in claim 2 wherein the weighted variables are developed using logistical regression to establish relationships between the dependent variable and independent variables.

7. The method as in claim 2 wherein the weighted variables are developed using discriminate analysis to establish relationships between the dependent variable and independent variables.

8. The method as in claim 2 wherein appropriateness of patient indicia is evaluated using the Hosmer-Lemeshow Goodness of Fit Analysis.

9. The method as in claim 1 wherein the potential chronic pain patients are identified with a patient mathematical expression generated by the chronic pain inference engine operating on the patient indicia and the chronic pain indication.

10. The method as in claim 9 wherein the patient indicia are monitored and for changes and the patient mathematical expression is updated when patient indicia change.

11. The method as in claim 9 wherein the patient mathematical expression is used to administratively categorize potential chronic pain patients.

12. The method as in claim 11 wherein the administrative categories are selected from the group consisting of positive-in, positive-out, probable-in, and probable-out.

13. The method as in claim 1 further comprising,

establishing categorization preferences that specify patient characteristics that are desired to be selected;

calculating the categorization preferences with each potential chronic pain patient'"'"'s mathematical expression to identify relationships between the categorization preferences and each potential chronic pain patient'"'"'s mathematical expression; and

,

categorizing each potential chronic pain patient based upon the relationships between the categorization preferences and each potential chronic pain patient'"'"'s mathematical expression.

14. The method as in claim 13 wherein the selection objectives are selected from the group consisting of potential chronic pain patients with pain attributable to their work environment, potential chronic pain patients unlikely to be compliant with treatment therapy, potential chronic pain patients unlikely to return to work, potential chronic pain patient suitable for low cost therapy, and potential chronic pain patient treatable by a primary care clinician.

15. The method as in claim 1 wherein the direct medical indicia are related to chronic pain in a known medical manner and recorded by a clinician.

16. The method as in claim 15 wherein the direct medical indicia are independent variables selected from the group consisting of primary diagnosis, associated secondary diagnosis, co-morbidities, drug treatment regimen, telephone consultations with a clinician, trauma episodes, palliative care, rehabilitative care, clinician office visits, emergency room visits, and hospitalizations.

17. The method as in claim 15 wherein the sources for direct medical indicia are selected from the group consisting of claims records, medical records, workers'"'"' compensation records, and employer records.

18. The method as in claim 1 wherein indirect medical indicia are a chronic pain co-morbidity that is recorded by a clinician.

19. The method as in claim 18 wherein the indirect medical indicia are independent variables selected from the group consisting of mental health condition, acute respiratory episodes, diabetes, and heart failure.

20. The method as in claim 18 wherein the sources for indirect medical indicia are selected from the group consisting of claims records, medical records, workers'"'"' compensation records, employer records, and patient surveys.

21. The method as in claim 1 wherein the non-medical indicia are independent variables selected from the group consisting of alcohol consumption, smoking status, weight gain, pain perception factors, life satisfaction measures, patient support structure, day-time distractions, marital relationship quality, personality profile, psychological profile, courtroom demeanor, reputation for truth and veracity, demeanor of associates, reputation of counsel, familial persuasion, financial needs, financial expectations, legal experience, personal injury history, family and friends injury history, cognitive ability, emotional maturity, and media reporting related to the indication.

22. The method as in claim 21 wherein the sources for non-medical indicia are selected from the group consisting of medical records, patient surveys, patient self-reports, employer databases, workers'"'"' compensation records, medical chart reviews, patient interviews, treating clinician interviews, and family member interviews.

23. The method as in claim 1 wherein the chronic pain indication is selected from the group consisting of Peripheral Neuropathy;

Stump Pain;

Phantom Pain;

Complex Regional Pain Syndrome Type I (Reflex Sympathetic Dystrophy);

Complex Regional Pain Syndrome Type II (Causalgia);

Central Pain;

Rheumatoid Arthritis;

Osteoarthritis;

Sickle Cell Arthropathy;

Stiff Man Syndrome;

Osteoporosis;

Guillain-Barre Syndrome;

Superior Pulmonary Sulcus Syndrome (Pancoast Tumor);

Pain of Skeletal Metastatic Disease of the Neck, Arm, or Shoulder Girdle;

Carcinoma of Thyroid;

Post Herpetic Neuralgia;

Syphilis (Tabes Dorsalis and Hypertrophic Pachymeningitis);

Primary Tumor of a Vertebral Body;

Radicular Pain Attributable to a Prolapsed Cervical Disk;

Traumatic Avulsion of Nerve Roots;

Primary Tumor of a Vertegral Body;

Radicular Pain Attributable to a Thoracic Disk;

Chemical Irritation of the Brachial Plexus;

Traumatic Avulsion of the Brachial Plexus;

Postradiation Pain of the Brachial Plexus;

Painful Arms and Moving Fingers;

Brachial Neuritis (Brachial Neuropathy, Neuralgic Amyotrophy, Parsonage-Turner Syndrome);

Raynaud'"'"'s Disease;

Raynaud'"'"'s Phenomenon;

Frostbite and Cold Injury;

Brythema Pernio (Chilblains);

Acrocyanosis;

Livedo Reticularis;

Volkmann'"'"'s Ischemic Contracture;

Thromboangiitis;

Intermittent Claudication;

Rest Pain;

Gangrene Due to Arterial Insufficiency;

Other Postinfectious and Segmental Peripheral Neuralgia;

Angina Pectoris;

Postmastectomy Pain Syndrome (Chronic Nonmalignant);

Late Postmastectomy Pain or Regional Carcinoma;

Segmental or Intercostal Neuralgia;

Chronic Pelvic Pain Without Obvious Pathology;

Pain from Urinary Tract;

Carcinoma of the Bladder;

Lumbar Spinal or Radicular Pain after Failed Spinal Surgery;

Spinal Stenosis (Cauda Equina Lesion);

Pain referred from Abdominal or Pelvic Viscera or Vessels Perceived as Sacral Spinal Pain;

Femoral Neuralgia; and

, Sciatica Neuralgia.

24. The method as in claim 23 wherein the source for chronic pain indications is the International Association for the Study of Pain (IASP) chronic pain guidelines.

25. The method as in claim 1 wherein the patient population is selected from the group consisting of payer database, employer database, clinician database, and workers'"'"' compensation database.

28. The computer software product as in claim 27, further comprising,

a fourth set of instruction when executed by the computer, cause the computer to establish categorization preferences that specify characteristic of patents that are desired to be categorized;

a fifth set of instruction when executed by the computer, cause the computer to calculate the categorization preferences with each potential chronic pain patient'"'"'s mathematical expression to identify relationships between the categorization preferences and each potential chronic pain patient'"'"'s mathematical expression; and

,

a sixth set of instruction when executed by the computer, cause the computer to categorize each potential chronic pain patient based upon the relationships between the categorization preferences and each potential chronic pain patient'"'"'s mathematical expression.

30. The method as in claim 29, further comprising

applying a sensitivity analysis model to the weighted variables to identify potential weighted variable changes to reduce errors in identifying chronic pain patients;

selecting at least weighted variable change from the potential weighted variable changes to apply to the weighted variables; and

,

modifying weighed variables to reflect greater or lesser relevance of patient indicia to reduce errors in identifying chronic pain patients.

26. A method for identifying and categorizing potential chronic pain patients, comprising:

accessing a chronic pain model having direct medical indicia, indirect medical indicia, non-medical indicia, and a chronic pain indication that are arranged logic structure, with weighted variables, and equations representing relationship between or among the variables;

applying the chronic pain model to a population to create a patient mathematical expression for each member of the population;

identifying potential chronic pain patients by comparing each patient mathematical expression to selection objectives;

establishing categorization preferences that specify characteristics of patents that are desired to be categorized;

calculating the categorization preferences with each potential chronic pain patient'"'"'s mathematical expression to identify relationships between the categorization preferences and each potential chronic pain patient'"'"'s mathematical expression;

categorizing each potential chronic pain patient based upon the relationships between the categorization preferences and each potential chronic pain patient'"'"'s mathematical expression; and

,

monitoring the potential chronic pain patient'"'"'s direct medical indicia, indirect medical indicia, and non-medical indicia for changes and updating the patient'"'"'s mathematical expression based upon changes to the potential chronic pain patient'"'"'s direct medical indicia, indirect medical indicia, and non-medical indicia.

27. A computer software product that includes a medium readable by a computer, the medium having stored thereon instructions for identifying patients in a population having a chronic pain condition, comprising:

a first set of instructions when executed by the computer, causes the computer access a chronic pain model having direct medical indicia, indirect medical indicia, non-medical indicia, and a chronic pain indication that are arranged logic structure, with weighted variables, and equations representing relationship between or among the variables;

a second set of instructions when executed by the computer, causes the computer to applying the chronic pain model to a population to create a patient mathematical expression for each member of the population; and

,

a third set of instructions when executed by the computer, cause the computer to identify potential chronic pain patients by comparing each patient mathematical expression to selection objectives.

29. A method for sensitivity analysis of a chronic pain patient model, comprising:

comparing the identified chronic pain patients with outside diagnosed chronic pain patient data to create a patient error list;

applying an error assessment model to the patient error list to identify the non-corresponding patient indicia that contributed to the errors;

applying a sensitivity analysis model to the non-corresponding patient indicia to identify potential patient indicia changes to reduce errors in identifying chronic pain patients;

selecting at least one patient indicia change from the potential patient indicia changes to apply to the patient indicia; and

,

modifying the patient indicia with the at least one selected patient indicia change.