Capturing person-specific self-reported subjective experiences as behavioral predictors
Disclosed methodologies provide improved predictors of patient treatment adherence by using person-specific subjective experience and social-environmental factors. Methodologies combine emotion and data sciences. Advanced tools capture, measure, store, and analyze self-report of subjective experiences using digital applications and platforms. Patient-specific data is obtained regarding emotional or affective determinants and social determinants for generating a calculated composite score of the patient's probability of adherence or achievement relative to target outcomes, e.g. adherence to treatment plans, wellness activities, etc. for a subject individual. Internal/subjective factors are judged by self-report measures designed to validly judge tested factors based on a patient adjusting continuously-variable graphical interfaces to capture and measure subjective experiences. Emotional characteristics may include perception and intensity in each category of sickness versus wellness, stress, depression, anxiety, pain, and feelings about most recent health provider/staff interaction (with determined intensity for choices of Delighted, Satisfied, Meh, Disappointed, Frustrated). Emotional characteristics may be considered among health, and social characteristics in measuring potential obstacles to adherence.
1. A methodology for predictively determining a patient's likelihood to adhere to a healthcare treatment plan for such patient, comprising:
creating a survey comprising a plurality of survey items related to selected determined obstacles to adherence;
interactively conducting the survey for a given patient by having the patient use a movable feature of a graphical interface on a display to respectively capture and definitively measure through non-verbal communication the patient's subjective experiences for each of the plurality of survey items, to form a set of data for the given patient for the corresponding plurality of survey items, wherein the graphical interface comprises digital graphic shape-shifting icons for such patient to view on said display, with the patient using the movable feature to manipulate the appearance of each respective icon through a range of appearances thereof so that it reflects the self-reported intensity of how such patient subjectively feels in response to each item of the patient survey; and
assessing the patient's set of data to determine a relative score for such patient for likelihood to adhere to a healthcare treatment plan.
2. A methodology as in claim 1 , wherein the definitive measurement captured with each icon corresponds directly to the final point of manipulation established by the patient through such range of appearances in response to a particular survey item.
3. A methodology as in claim 2 , wherein the shape-shifting icons are pre-validated for a selected population of patients in which the given patient is a member, to validate that each icon and its range of appearances represents the subject matter of its associated item and attendant range of intensities thereof.
4. A methodology as in claim 1 , wherein the graphical interface renders scaled values to capture and definitively measure subjective experiences by having the patient use a movable bar to manipulate at least one of each respective icon.
5. A methodology as in claim 1 , wherein assessing the patient's set of data includes establishing a predictive score for the given patient indexed relative to ranges of scoring of sets of data for responses to items involving the same shape-shifting icons as have been used in the survey for the given patient and pre-validated for a selected population of patients in which the given patient is a member.
6. A methodology as in claim 1 , wherein assessing the patient's set of data includes establishing one of a relatively high, medium, or low probability of adherence to a healthcare treatment plan for the given patient.
7. A methodology as in claim 1 , wherein assessing the patient's set of data includes establishing a specific score within an indexed range to relatively assess the probability of adherence to a healthcare treatment plan for the given patient.
8. A methodology as in claim 7 , wherein the indexed range comprises a scaled range from between 0 and 100.
9. A methodology as in claim 1 , further including reporting the determined relative score to at least one of the given patient, healthcare staff supporting the patient, researchers, administrators, payors, and supportive associates of the given patient.
10. A methodology as in claim 9 , further including subsequently interactively conducting the same or a different survey for the given patient at a later point in time, and reporting an updated relative score to at least one of the given patient, healthcare staff supporting the patient, researchers, administrators, payors, and supportive associates of the given patient.
11. A methodology as in claim 1 , further including:
creating a database of measures of the patient's health status indicators and external social and economic variables to likelihood of adherence, to form a set of fact-based data for the given participant; and
collectively assessing the patient's internal self-reported data and fact-based data to determine a relative score for the given patient for likelihood to adhere to a healthcare treatment plan for the given patient.
12. A methodology as in claim 11 , wherein the patient's health status indicators include at least one of body mass index (BMI), blood A1c levels, and Rx fill/refill data for the given patient.
13. A methodology as in claim 1 , wherein the healthcare treatment plan comprises at least one of a plan of treatment for a particular condition of a given patient and a wellness activities plan for a given patient.
14. A methodology as in claim 1 , wherein the items include at least one of a question, an image, a statement, a sound bite, and a video file presented to the patient for capture of the patient's subjective response thereto.
15. A methodology for predictively determining a participant's likelihood to adhere to an activity plan for such participant, comprising:
interactively conducting a survey for a given participant by having the participant use a movable feature of a graphical interface on a display to respectively capture and definitively measure through non-verbal communication the participant's self-reported internal subjective feelings in response to a plurality of survey items concerning affect-based variables to likelihood of adherence, to form a set of internal self-reported data for the given participant, wherein the graphical interface comprises digital graphic shape-shifting icons for such participant to view on said display, with the participant using the movable feature to manipulate the appearance of each respective icon through a range of appearances thereof so that it reflects the self-reported intensity of how such participant subjectively feels in response to each item of the participant survey;
creating a database of measures of the participant's external social and economic variables to likelihood of adherence, to form a set of external fact-based data for the given participant; and
collectively assessing the participant's internal self-reported data and external fact-based data to determine a relative score for such participant for likelihood to adhere to an activity plan.
16. A methodology as in claim 15 , wherein:
said affect-based variables comprise determined obstacles to achievement of activities in an activity plan.
17. A methodology as in claim 16 , wherein said digital graphic displays comprise a plurality of shape-shifting icons with at least one icon for each respective survey item and a movable element for the participant to manipulate a shape-shifting icon through a range of appearances designed to correlate with the intensity of the participant's feelings in response to a survey item, with the appearances of the shape-shifting icons pre-validated for a population of participants in which the given participant is a member, to validate that each icon represents the subject matter of its associated item and attendant range of intensities thereof.
18. A methodology as in claim 15 , wherein collectively assessing the participant's internal self-reported data and external fact-based data includes establishing one of a relatively high, medium, or low probability for such participant for likelihood to adhere to an activity plan.
19. A methodology as in claim 15 , wherein collectively assessing the participant's internal self-reported data and external fact-based data includes establishing a specific score within an indexed range to relatively assess the probability for such participant for likelihood to adhere to an activity plan.
20. A methodology as in claim 19 , wherein the indexed range comprises a scaled range from between 0 and 100.
21. A methodology as in claim 15 , wherein the participant comprises a potential consumer for a given product, and the survey items relate to a particular product or service of potential interest to the potential consumer, as part of evaluating customer experiences or conducting consumer research relative to such particular product or service.
22. A methodology as in claim 16 , wherein the participant comprises a patient and the activity plan comprises a health treatment plan for the patient.
23. A methodology as in claim 22 , further including subsequently interactively conducting the same or a different survey for the given patient at a later point in time, and reporting an updated relative score to at least one of the given patient, healthcare staff supporting the patient, researchers, administrators, payors, and supportive associates of the given patient.
24. A methodology as in claim 22 , wherein the external social and economic variables to likelihood of adherence for a patient comprise social/economic factors experienced by an individual patient that negatively impact medical treatment plan adherence, including at least one of low socioeconomic status, low health literacy, low levels of education, low levels of social support, unemployment, housing instability, family dysfunction, barriers with transportation to medical care, high medication costs, and negative cultural beliefs about medical treatment.
25. A methodology as in claim 16 , wherein the internal subjective feelings which are variables to likelihood of adherence for a patient include self-reported felt sense and intensity thereof for at least one of wellness versus illness, stress, depression, anxiety, pain, and loneliness of the patient.
26. A methodology as in claim 25 , wherein the internal subjective feelings further include a patient's self-reported sense and intensity of satisfaction with the patient's most recent health provider/staff interaction.
27. A methodology as in claim 15 , wherein the survey items include at least one of a question, an image, a statement, a sound bite, and a video file presented to the participant for capture of the participant's subjective response thereto.
28. A system for predictively determining a given patient's likelihood to adhere to a healthcare treatment plan for such patient, comprising:
a memory;
a display; and
a processor coupled to the memory programmed with executable instructions, the instructions including:
a patient survey comprising a plurality of survey items to be administered to a given patient and related to selected determined obstacles to adherence to a healthcare treatment plan for such patient,
a patient graphical interface comprising digital graphic shape-shifting icons for such patient to view on said display, and to capture and definitively measure through non-verbal communication subjective experiences thereof by such patient by having such patient use a movable feature to manipulate the appearance of each respective icon through a range of appearances thereof so that it reflects the self-reported intensity of how such patient subjectively feels in response to each item of the patient survey, and
an assessing component, for assessing such patient's set of self-reported responses to determine a relative score for such patient for likelihood to adhere to a healthcare treatment plan.
29. A system as in claim 28 , wherein the system is implemented via a hardware and software platform comprising a plurality of network-based non-transitory storage devices, servers, and processors, which are accessible by authorized users.
30. A system as in claim 29 , wherein the system includes at least one network-based non-transitory storage device for being accessed by authorized users, for the update and storage therein of data on at least one particular patient concerning at least one of background external health, social, and economic variables to likelihood of adherence for such particular patient.
31. A system as in claim 29 , wherein the system includes at least one network-based non-transitory storage device for being accessed by at least one particular patient, for the update and storage therein of data on at least one of definitively measured subjective experiences for such particular patient in response to at least one item of the patient survey.
32. A system as in claim 30 , wherein:
the system includes at least one network-based non-transitory storage device for being accessed by at least one particular patient, for the update and storage therein of data on at least one of definitively measured subjective experiences for such particular patient in response to at least one item of the patient survey; and
the assessing component is further operative for collectively assessing such patient's set of responses along with stored data on such patient concerning background external health, social, and economic variables to likelihood of adherence, to determine a relative score for such patient for likelihood to adhere to a healthcare treatment plan.
33. A system as in claim 32 , wherein the assessing component is further operative for collectively assessing such patient's set of self-reported responses normalized relative to stored data of anonymized results from a plurality of other patients with common or similar background data.
34. A system as in claim 32 , wherein the assessing component is further operative for storing on at least one network-based non-transitory storage device the self-reported responses and relative score for such patient for likelihood to adhere to a healthcare treatment plan, to be accessed by authorized users.
35. A system as in claim 34 , wherein the assessing component is further operative for storing on such at least one network-based non-transitory storage device the self-reported responses and relative scores for such patient, based on repeated administrations of the same or a different patient survey to such patient, for likelihood to adhere to a healthcare treatment plan data for such patient, to be accessed by authorized users.
36. A system as in claim 32 , wherein the system further includes a reporting device for preparing a report of data otherwise stored on at least one network-based non-transitory storage device regarding the self-reported responses and relative score for such patient for likelihood to adhere to a healthcare treatment plan, and for transmitting the report to selected entities.
37. A system as in claim 36 , wherein the selected entities comprise at least one of a given patient, healthcare staff supporting the given patient, researchers, administrators, payors, and supportive associates of the given patient.
38. A system as in claim 37 , wherein the reporting device is operative for preparing a report covering time periods determined by the selected entity to receive the report.
39. A system as in claim 28 , wherein the patient movable feature comprises a patient movable bar to manipulate at least one of each respective icon so that it reflects how the patient feels in response to each item.
40. A system as in claim 28 , wherein the shape-shifting icons are pre-validated for a selected population of patients in which the given patient is a member, to validate that each icon and its range of appearances represents the subject matter of its associated item and attendant range of intensities thereof.
41. A system as in claim 40 , wherein the shape-shifting icons are pre-validated through an iterative icon design process which focuses on a patient's perceived resonance between an icon and the corresponding subject matter being surveyed through use of such icon.
42. A system as in claim 41 , wherein the iterative icon design process includes use of a forced-choice validation engine paradigm to study the correlation between an icon design and the subject matter to be surveyed through use of such icon.
43. A system as in claim 42 , wherein the iterative icon design process further includes use of an intensity rating validation paradigm to study the correlation between an icon design and the range of intensities of a patient's feelings to be represented by such icon.
44. A system as in claim 28 , wherein the healthcare treatment plan comprises at least one of a plan of treatment for a particular condition of a given patient and a wellness activities plan for a given patient.
45. A system as in claim 28 , wherein the shape-shifting icons respectively comprise icons with changing aspects each including at least one of construction, display, and use including size, color, shape, opacity, and data input methods.
46. A system as in claim 28 , wherein the patient graphical interface captures and definitively measures subjective experiences by rendering scaled values by having the patient use a movable bar to manipulate at least one of each respective icon so that it reflects how the patient feels in response to each item.
47. A system as in claim 28 , wherein the relative score includes one of a relatively high, medium, or low likelihood to adhere to a healthcare treatment plan for such patient.
48. A system as in claim 28 , wherein the relative score includes a specific score within an indexed range to relatively assess the likelihood to adhere to a healthcare treatment plan for such patient.
49. A system as in claim 48 , wherein the indexed range comprises a scaled range from between 0 and 100.
50. A system as in claim 28 , wherein the survey items include at least one of a question, an image, a statement, a sound bite, and a video file presented to the patient for capture of the patient's subjective response thereto.
51. A methodology as in claim 1 , wherein assessing the patient's set of data includes establishing a specific score within an indexed range to relatively assess the probability of adherence to a healthcare treatment plan and cohorts for the given patient.
52. A methodology as in claim 51 , wherein the indexed range comprises a scaled and normalized range from between 0 and a predetermined top scale number.
53. A methodology as in claim 52 , wherein the predetermined top scale number falls into a range from 1 to 100.
54. A methodology as in claim 22 , wherein creating a database of measures further includes creating a database of measures of the participant's external social and economic variables, and external environment variables affecting a patient's likelihood of adherence, to form the set of external fact-based data for the given participant.
55. A methodology as in claim 54 , wherein the external social and economic variables and external variables to likelihood of adherence for a patient comprise social/economic/environmental factors experienced by an individual patient that negatively impact medical treatment plan adherence, including at least one of low socioeconomic status, low health literacy, low levels of education, low levels of social support, unemployment, housing instability, poor environmental conditions, family dysfunction, barriers with transportation to medical care, high medication costs, and negative cultural beliefs about medical treatment.
56. A methodology as in claim 19 , wherein the indexed range comprises a scaled and normalized range from between 0 and a predetermined top scale number.
57. A methodology as in claim 56 , wherein the predetermined top scale number falls into a range from 1 to 100.
58. A system as in claim 48 , wherein the indexed range comprises a scaled and normalized range from between 0 and a predetermined top scale number.
59. A system as in claim 58 , wherein the predetermined top scale number falls into a range from 1 to 100.