Research on the Integration of the DIKWP Model with the Information Field and Energy Field in Active Medicine
Yucong Duan
Benefactor: Zhendong Guo
International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation(DIKWP-SC)
World Artificial Consciousness CIC(WAC)
World Conference on Artificial Consciousness(WCAC)
(Email: duanyucong@hotmail.com)
The Relationship Between the Information Field and the DIKWP Model
In the theory of active medicine, the "information field" is regarded as a holographic representation of the human body's health status, encompassing all levels from raw data to higher-level wisdom ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). The DIKWP model divides this process into five core dimensions: Data (D), Information (I), Knowledge (K), Wisdom (W), and Purpose (P). Among these, the information field primarily corresponds to the first four dimensions—data, information, knowledge, and wisdom ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). Below are the meanings of each level in the context of health:
Data (D): This refers to raw health-related data from the human body, such as physiological indicators (blood pressure, heart rate, etc.), genetic sequences, EEG/MEG signals, and more ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). These unprocessed data form the foundation of the information field, recording objective facts about the body's state. For example, a patient’s daily blood pressure readings constitute a series of raw data that forms the base layer of the health information field.
Information (I): Through processing and analyzing the data, meaningful patterns and metrics are extracted ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). This step transforms "raw data" into "meaningful information." For instance, from continuous blood pressure data, we can calculate average day and night blood pressure, fluctuation ranges, etc.; if it is found that the blood pressure consistently exceeds normal ranges, this becomes "hypertension information" ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). The information layer reveals the characteristics of health status hidden within the data, such as abnormal indicators and trend directions, providing a basis for further decision-making.
Knowledge (K): Building on the information, known laws and theories from the medical domain are integrated to elevate fragmented information into systematic knowledge ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). This includes modern medical knowledge (such as pathophysiology, pharmacology) and traditional medical experiences (such as Chinese meridian theory, Ayurvedic principles), among other multidisciplinary knowledge ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). The knowledge layer allows us to explain the reasons and mechanisms behind the information and guide health management accordingly. For example, based on medical knowledge, we know that persistent hypertension may be related to genetic factors, high-salt diets, stress, etc., thereby understanding the causes and guiding interventions.
Wisdom (W): At the level of knowledge, ethical and value judgments are introduced to make high-level decisions regarding health information ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). The wisdom layer means not only relying on technology and knowledge for decision-making but also considering the overall well-being of the patient, humane care, and long-term impacts ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). For example, when devising intervention strategies for hypertensive patients, the wisdom layer considers the patient's lifestyle preferences, economic situation, and willingness to comply, creating personalized, sustainable plans rather than simply following guidelines ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). Wisdom ensures that health management decisions are reasonable and humane, avoiding potential deviations from purely technical approaches ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields).
The information field spans the semantic space from data to wisdom ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). For example, a middle-aged person wearing a health monitoring device collects daily step counts, sleep duration, and other data (D); these data are analyzed by an application program to produce reports on lifestyle, such as "average daily steps below 5000, less than 7 hours of sleep" (I). Doctors combine medical knowledge to determine that this pattern indicates an increased cardiovascular risk (K) and, at the wisdom level, consider the individual's work stress and family responsibilities, suggesting feasible exercise plans and work rhythm adjustments (W). In this process, the information field connects raw health data with high-level decision-making, allowing health status to be perceived and understood from multiple angles.
The impact of the information field on health status lies in its provision of a feedback regulation mechanism: changes in the body are reflected in the data, processed through information and knowledge to become decision-making bases, which then guide health interventions, acting back on the body. For example, when a warning signal like "elevated blood pressure" appears in the information field, the healthcare system takes action accordingly, such as advising the patient to reduce salt intake and increase physical activity ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). These interventions adjust the energy balance within the patient's body (see next section), subsequently leading to a decrease in blood pressure; the new normalized data feeds back into the information field, indicating health improvement ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). This closed-loop regulation driven by the information field enables active medicine to sense and influence health status in real-time, achieving early prevention and timely correction of diseases ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields).
In summary, the information field uses the hierarchical structure of the DIKWP model to transform physiological signals into interpretable health landscapes ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). It encompasses both objective data and subjective cognition and wise judgment, playing a comprehensive role in characterizing and guiding health status. In practical cases, anomalies in the information field often serve as precursors to disease: for example, abnormal ECG data (D) generates arrhythmia information (I), combined with knowledge to judge possible atrial fibrillation (K), while wise decision-making requires immediate intervention to prevent stroke (W). This illustrates how subtle changes in the information field can foreshadow and influence health status.
The Energy Field and the 25 Transformation Modules of DIKWP*DIKWP
In active medicine, the "energy field" refers to the biological energy state of the human body, including physiological energy (e.g., metabolic levels), psychological energy (e.g., emotions, stress), and broader vitality. The energy field is not separate from the information field; it primarily corresponds to the higher dimensions of the DIKWP model—Wisdom (W) and Purpose (P) layers ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). Specifically, the energy field represents the dynamic equilibrium state of the body's systems, which is regarded as the fundamental manifestation of health. Decisions at the Wisdom and Purpose levels directly involve how to maintain or restore this energy balance ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields).
Positive Energy and the Analogy of "Dao": In the DIKWP model, the positive energy state of the energy field can be likened to the concept of "Dao" in Chinese philosophy—an ideal state of harmony between body and mind ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). The Wisdom layer focuses on maintaining the balance of physiological and psychological energy through appropriate lifestyle habits, mental adjustments, and social support, akin to following the path of health "Dao." The Purpose layer defines the goals of health management, ensuring that all actions are directed toward achieving overall balance in the energy field, reflecting the pursuit of "Dao" ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). For example, if a person’s energy field remains imbalanced over time (e.g., chronic fatigue, anxiety), the Wisdom level would advocate returning to regular routines and inner tranquility, rebuilding positive energy.
To integrate the information field with the energy field more closely, the DIKWP model introduces the DIKWPDIKWP interaction structure, resulting in 5×5=25 possible transformation modules ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields - ResearchGate). Unlike traditional linear hierarchical models, DIKWPDIKWP emphasizes bidirectional, networked interactions between layers (Interpreting Duan Yuc聪's active medicine theory from the perspective of the DIKWP model - ScienceNet). Each conversion between two dimensions is considered a functional module, including transformations within the same layer (e.g., Data to Data) and across levels (e.g., Data to Information, Knowledge to Wisdom) (Research on Mapping LLM Black Box Evaluation Mechanism to DIKWPDIKWP Modules - Zhihu Column) (Cognitive Impairment Assessment Method Based on DIKWPDIKWP Framework - Beginner Version - Blog by Duan Yuc聪). These 25 transformation modules encompass all possible pathways for health information to change forms (Research on Mapping LLM Black Box Evaluation Mechanism to DIKWP*DIKWP Modules - Zhihu Column). Below, we break down these 25 modules and explain their roles with examples of health optimization:
(1) Transformation Modules Initiated by the Data Layer:
Raw data can be processed along different paths, affecting itself or higher levels:
D→D (Data to Data): Refers to transformations and cleaning within the data layer, including data acquisition, calibration, and integration. In health management, this manifests as quality control and organization of raw physiological data. For instance, wearable blood pressure monitors need calibration before use to ensure accurate readings (Research on Mapping LLM Black Box Evaluation Mechanism to DIKWP*DIKWP Modules - Zhihu Column). Similarly, consolidating personal health data dispersed across devices or medical institutions into a unified record is part of the D→D module. This module ensures a solid and reliable foundation for subsequent analysis, akin to adhering to objective norms in recording physical states.
D→I (Data to Information): Refers to extracting meaningful information from raw data ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). In health optimization, this step is crucial. For example, analyzing continuous weekly blood glucose monitoring data (D) can produce information about blood glucose fluctuation ranges and post-meal peaks (I), assessing glycemic control ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). Another example: sleep monitoring devices record brainwave and body movement data (D), and algorithms convert them into sleep stages and quality scores (I). The D→I module distills "signals" from vast "noise," providing clues for identifying health issues.
D→K (Data to Knowledge): Sometimes, raw data analyzed deeply can generate new knowledge or enrich existing knowledge bases. This is particularly evident in the era of big data and AI (Research on Diagnosis, Treatment, and Long-Term Management of Degenerative Diseases Based on DIKWP - Blog by Duan Yuc聪). For instance, large-scale studies analyzing genome data of tens of thousands of individuals reveal new disease-related mutation sites, forming new medical knowledge (e.g., discovering a gene variant that increases Parkinson's disease risk). Similarly, hospitals accumulate extensive patient imaging and clinical data, using machine learning to identify new diagnostic patterns, aiding doctors' decision-making. The D→K module elevates data to generalized medical experiences and rules, significantly advancing medicine.
D→W (Data to Wisdom): Refers to situations where raw data is used directly for wise decision-making. Typically, decisions require information and knowledge, but in certain "quick reflex" or highly automated scenarios, systems may make wise responses based solely on data. For example, implantable cardioverter-defibrillators monitor heart electrical activity (D) and automatically deliver shocks upon detecting life-threatening arrhythmias, without human intervention—a life-saving decision considered an act of wisdom (W). Similarly, well-trained AI models can provide near-expert-level suggestions based on real-time patient data (Research on Diagnosis, Treatment, and Long-Term Management of Degenerative Diseases Based on DIKWP - Blog by Duan Yuc聪). The D→W module emphasizes rapid, highly automated decision processes, functioning effectively in critical situations and intelligent systems.
D→P (Data to Purpose): Raw data can influence health goals and intentions. For instance, continuous fitness tracking data (steps, calorie burn, etc.) showing significant deviations below healthy standards might prompt someone to set a goal of "exercising one hour daily" (P). Alternatively, genetic testing revealing elevated risks for specific cancers may directly alter one's health intentions—increasing focus on regular check-ups and preventive interventions. The D→P module illustrates how objective facts shape subjective motivations: when data signals warnings or incentives, individuals or healthcare teams adjust health management directions and ultimate purposes accordingly.
(2) Transformation Modules Initiated by the Information Layer:
Information, as processed data, can affect data collection downwardly or promote knowledge formation upwardly:
I→D (Information to Data): The information layer can guide new data collection or reorganize existing data. For example, a doctor, based on patient consultation information (e.g., frequent headaches, poor sleep), may determine the need for further blood pressure data, arranging 24-hour ambulatory blood pressure monitoring. This is a case of information-driven data collection: existing information suggests the need for more data to verify hypotheses. Similarly, a health app prompting users about "recent insufficient exercise" (information) motivates them to start logging daily walking data. The I→D module ensures targeted and purposeful data collection, supplementing the information field with key raw data.
I→I (Information to Information): Refers to cross-integration and refinement of information, generating higher-level new information. For example, combining different dimensions of health information can form composite indicators: integrating heart rate variability and sleep quality information results in a "stress level" indicator. Another example: doctors synthesizing multiple lab result pieces of information to derive a "patient metabolic syndrome risk index." The I→I module aggregates and refines information, similar to summarizing and concluding within the information layer. In health optimization, this helps uncover comprehensive health pictures beyond what single pieces of information reveal individually—like isolated normal blood sugar or blood pressure readings potentially indicating pancreatic function issues.
I→K (Information to Knowledge): Reliable health information can lead to summarization and validation of medical knowledge. For example, a series of patients' treatment response information can form an experience: "a certain drug works better in Asian hypertensive patients." This is essentially moving from specific information to group knowledge. In research, summarizing case series information often contributes to publishing medical reports, enriching clinical knowledge bases ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). The I→K module also occurs at the individual level: patients internalize scattered information from multiple health check-ups, understanding the importance of exercise for their blood pressure—transforming fragmented information into personalized health knowledge.
I→W (Information to Wisdom): Refers to directly using health information for high-level decision-making and judgment. Clinically, critical information often requires immediate ethical and value-based decisions. For example, prenatal examination information showing severe fetal abnormalities (e.g., vital organ underdevelopment) prompts doctors and families to enter wise decision-making mode, considering moral and legal factors, deciding whether to terminate pregnancy. In such cases, that critical piece of information directly triggers high-level discussion. Similarly, when patients show rapidly deteriorating vital signs, medical teams may initiate end-of-life care based on available information without full knowledge details. The I→W module highlights the urgent value of information: when sufficiently strong or contextually pressing, it bypasses routine analysis, directly invoking wise responses.
I→P (Information to Purpose): Changes in health information often affect personal and healthcare team health intentions and goal adjustments. For instance, weight managers seeing excessive calorie intake information for the week may immediately adjust next week’s weight loss goals (more exercise or reduced calorie intake)—information prompting intent updates. Similarly, hospitals discovering recent flu case surges via information systems adjust prevention intentions, planning emergency vaccinations or educational campaigns to reduce transmission. The I→P module demonstrates information feedback on goal setting: as the health information field continuously updates, health intentions dynamically optimize to ensure alignment with current needs.
(3) Transformation Modules Initiated by the Knowledge Layer:
As systematized medical cognition, knowledge affects various levels like data and information, guiding practice:
K→D (Knowledge to Data): Medical knowledge influences what data we collect and how. Clinical guideline knowledge advises regular colonoscopy screenings for those aged 50+, leading to increased colonoscopy data collection in high-risk age groups. Doctors knowing a patient has a family history of diabetes (knowledge) might recommend periodic fasting blood sugar and HbA1c checks despite current normal readings. The K→D module embodies knowledge-guided monitoring: our understanding of disease mechanisms and risk factors determines data collection strategies, preventing diseases proactively.
K→I (Knowledge to Information): Medical knowledge helps interpret and filter information. When faced with abundant health data, knowledge guides us on which information matters. For instance, doctors know that "2-hour postprandial blood glucose" reflects sugar metabolism better than "30-minute postprandial blood glucose," thus selecting 2-hour values as judgment criteria. This uses knowledge to filter data, forming key health information. Similarly, radiologists use anatomical and pathological knowledge to highlight suspicious lesions in complex images. The K→I module ensures scientifically valid information extraction, avoiding missed important clues or irrelevant information interference.
K→K (Knowledge to Knowledge): Interaction at the knowledge level mainly involves updating and merging knowledge. Medical knowledge isn’t static; new knowledge from different sources integrates into comprehensive knowledge. Combining Western medicine’s understanding of diabetic complications with Traditional Chinese Medicine’s "Xiao Ke" syndrome creates holistic diabetes management knowledge, enhancing prevention strategies ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). A general practitioner might combine nutritional knowledge with clinical pharmacology, discovering dietary habits affecting drug efficacy, forming new medication guidelines. The K→K module promotes multidisciplinary knowledge integration, fostering medical innovation and personalized treatment.
K→W (Knowledge to Wisdom): Rich knowledge reservoirs underpin wise decisions, but wisdom involves value judgments. K→W applies professional knowledge to specific contexts, forming prudent choices. For example, handling a complex cancer case requires oncology, genetics, psychology knowledge (K), then considering patient wishes, family factors (W), ultimately deciding treatment. Here, knowledge must be applied morally and humanely ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). Early in the COVID-19 pandemic, public health experts used past infectious disease knowledge (K) and combined social ethics (W) to suggest social distancing and mask policies—wisdom stemming from knowledge. The K→W module shows mere knowledge isn’t enough; placed in a wisdom framework, it translates into truly beneficial actions.
K→P (Knowledge to Purpose): Medical knowledge directly impacts people’s health goals and intention setting. Public awareness campaigns inform about smoking dramatically increasing lung cancer risk (knowledge), leading many to quit smoking or avoid secondhand smoke exposure (intention formed). A doctor mastering latest rehabilitation medicine knowledge might aim to apply it in more stroke patient recovery plans, improving independence. The K→P module embodies knowledge-action unity: gaining new health-related knowledge adjusts pursuits and plans, aligning with updated understanding. In active medicine, continuously updated knowledge guides higher-level goal settings, shifting from "treating illness" to "preventing illness"—a forward-looking health management vision driven by knowledge.
(4) Transformation Modules Initiated by the Wisdom Layer:
Wise decisions consider both knowledge and values; interactions from this level often involve adjusting the entire health system:
W→D (Wisdom to Data): Wise decisions can feedback to basic data collection to validate or implement decisions. For example, a wise doctor decides conservative treatment for a high-risk pregnant woman with close monitoring, increasing daily fetal heart rate and weekly ultrasound data collection frequency to support decision-making. Similarly, policy-level wise decisions (e.g., strengthening infectious disease monitoring in regions) drive grassroots data collection. The W→D module illustrates how high-level decisions create new data needs: ensuring correct execution often requires returning to the data layer for real-time, frontline information, allowing timely adjustments.
W→I (Wisdom to Information): Wise insights change views on important information, redefining priorities ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). For instance, in intensive care units, a senior expert (with rich experience and benevolence) might advise focusing less on minor biochemical fluctuations and more on patient complexion and subjective feelings. Thus, subjective pain levels become prioritized information. This uses wisdom to discern and refine information: what’s most meaningful in the current context. During epidemics, public health decision-makers might include “people’s mental health” in daily briefings, recognizing its importance. The W→I module reflects how people-oriented wisdom influences the information field, aligning with overall interests and long-term considerations.
W→K (Wisdom to Knowledge): High-level wisdom feedback sometimes prompts reflection and revision of existing knowledge. Past medical knowledge overly emphasized disease biological indicators, neglecting patients’ quality of life. With rising medical humanities wisdom, the industry revisits knowledge frameworks, incorporating quality of life measures, forming a "patient-centered" new knowledge paradigm ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). In multidisciplinary discussions on complex cases, an expert’s unique insight might break existing knowledge molds, leading teams to new understandings—precipitating improved medical knowledge. The W→K module reveals that knowledge systems aren’t closed; continuous wisdom emergence drives knowledge evolution, making it more complete and humane.
W→W (Wisdom to Wisdom): Interaction within the wisdom layer involves reflection and enhancement. In medical practice, expert dialogues and doctors’ experience summaries reflect wisdom influencing wisdom. For example, two chief physicians discussing difficult cases share handling experiences, enhancing each other’s decision-making wisdom. Doctor-patient communication mutually inspires intuitive and professional wisdom, finding treatments fitting patient values. Encouraging multi-party participation and experience feedback in active medicine achieves a positive W→W cycle—medical decisions grow wiser, systems more learning-capable (Research on Mapping LLM Black Box Evaluation Mechanism to DIKWP*DIKWP Modules - Zhihu Column) ((PDF) Custom Optimization Strategy for DeepSeek Based on DIKWP-DIKWP Conversion Modules). This module serves as a metacognitive process: continually reviewing decision quality makes wisdom increasingly approach the "Dao" realm.
W→P (Wisdom to Purpose): Values and foresight guided by wisdom ultimately manifest as clear health intentions and visions ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). When individuals or organizations reach consensus at the wisdom level, health goals often adjust to better fit fundamental interests. For example, a wise doctor recognizing prevention's importance over treatment (wisdom realization) initiates community chronic disease screening projects, expanding professional focus from treating to preventing illnesses (intention shift). Similarly, wisdom thinking leads patients to appreciate mind-body balance (understanding health essence), resolving to cultivate long-term meditation and exercise habits for harmonious goals ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). The W→P module marks wisdom ascending to vision: profound insights guide higher-level, comprehensive health pursuits, like "holistic physical, mental, spiritual health" intentions, summarized from previous efforts ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields).
(5) Transformation Modules Initiated by the Purpose Layer:
Purpose represents ultimate goals, motivation, and mission, influencing the entire system’s operation:
P→D (Purpose to Data): Clear health management intentions determine data collection and monitoring strategies ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). For example, a diabetic patient aiming to control blood glucose includes daily morning and evening blood glucose monitoring in their routine. Driven by this intention, they might purchase advanced glucometers or use continuous glucose monitoring devices for comprehensive data. Nationally, public health intentions to eradicate certain infectious diseases establish large-scale data networks to track cases. The P→D module embodies the principle “Where there’s a will, there’s a way”: clear goals activate and sustain related data actions, providing quantitative support for goal achievement.
P→I (Purpose to Information): Strong health intentions guide attention to specific information, extracting goal-relevant content from data. For example, someone wanting to lose weight (clear intention) pays particular attention to daily calorie intake and expenditure, recording progress. Intentions act as filters, actively capturing goal-relevant information amidst complex data. Hospitals aiming to improve patient satisfaction extract "average waiting time" and "complaint rate" information metrics from operational data. The P→I module means goals direct information collection and interpretation, focusing the information field on intent-related content.
P→K (Purpose to Knowledge): New health intentions often necessitate acquiring or seeking relevant knowledge support. Goals drive learning and knowledge creation. For instance, someone aspiring to run marathons (health intention) actively learns training and nutrition knowledge, consulting coaches for necessary knowledge reserves. Similarly, public health aiming to eliminate malaria invests resources researching transmission and prevention. The P→K module illustrates “Mission leads learning”: clear intentions fill knowledge gaps or recontextualize existing knowledge for goal achievement, providing intellectual assurance.
P→W (Purpose to Wisdom): Clear, significant goals strongly guide wise decision-making, ensuring all decisions revolve around the goal ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). Here, wisdom manifests as steadfast value orientation and ethical choices. A doctor committed to "patients first" consistently weighs decisions based on maximum patient benefit (wisdom demonstration). Someone determined to quit drinking (intention) chooses accordingly in various scenarios—self-discipline blending wisdom and willpower. The P→W module reflects “Original intention” guiding choices: pure, strong intentions help persist in correct directions amid numerous options, stabilizing wise decisions, keeping them aligned with healthy paths.
P→P (Purpose to Purpose): Evolution within the purpose layer generates new, higher goals or adjusts existing ones during goal achievement. Health journeys often proceed stage-wise: a sedentary person initially aims to "exercise three times weekly"; achieving this spawns new intentions like "participate in half-marathons." Medical teams reducing hospital infection rates might upgrade intentions to "establish zero-tolerance infection culture." The P→P module represents spiraling goal management: achieving intentions isn’t the endpoint but a new starting point for the next health milestone. In active medicine, continuous reflection and evaluation (guided by the wisdom layer) allow revising or elevating health goals, gradually approaching holistic health ideals ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields).
In summary, the 25 transformation modules of DIKWPDIKWP comprehensively cover the flow and transformation of health information and energy across different levels (Research on Mapping LLM Black Box Evaluation Mechanism to DIKWPDIKWP Modules - Zhihu Column). In active medicine practice, multiple module combinations often optimize health simultaneously. For example, managing a hypertensive patient might involve collecting daily blood pressure data via home monitors (D→D), analyzing circadian rhythm information (D→I), determining high-salt diet causes via medical knowledge (I→K), deciding medication with minimal lifestyle changes considering busy schedules (K→W), and collaboratively setting blood pressure reduction goals (W→P). Implementation involves weekly adjustments to medication doses and lifestyle details based on blood pressure information (information-driven energy regulation ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields)), ultimately achieving stable blood pressure through persistent medication adherence and gradual lifestyle improvements. This integrated scheme spans multiple DIKWP modules: data to information monitoring, knowledge to wisdom decision-making, wisdom to purpose goal guidance, and continuous information-driven adjustments. It is precisely these modules' comprehensive optimization that ensures treatment effectiveness and personalization ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields).
3. DIKWP Portraits of Diseases and Deviation Analysis
Within the framework of active medicine, we can create DIKWP portraits of diseases by describing their characteristics across the dimensions of Data (D), Information (I), Knowledge (K), Wisdom (W), and Purpose (P). This analysis helps identify deviations from the ideal state of health and assesses the impact of disease on the holistic health view encapsulated in the "Dao-De-Ren-Yi-Li" paradigm derived from Eastern philosophy. "Dao-De-Ren-Yi-Li" corresponds to different aspects of health: Dao represents overall harmony and the path to health; De reflects inner order and integrity (analogous to positive energy in body and mind); Ren corresponds to care and coordination between individuals and internal bodily systems; Yi involves rationality and appropriateness in decision-making; and Li manifests in the orderly conduct of daily behaviors and physiological rhythms. Below, we analyze two common neurodegenerative diseases—Alzheimer's Disease (AD) and Parkinson's Disease (PD)—to illustrate their DIKWP profiles and deviations from "Dao-De-Ren-Yi-Li."
DIKWP Profile Deviations in Alzheimer's Disease
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder characterized by memory loss and comprehensive cognitive decline. Its DIKWP profile can be described as follows:
Data Layer (D): Patients exhibit significant abnormalities in brain imaging and biomarker data. MRI or CT scans reveal global brain atrophy, particularly in the hippocampus, with deepened sulci and enlarged ventricles (File:Brain-ALZH.png - Wikimedia Commons). Brain metabolic PET imaging shows decreased metabolism in multiple brain regions (Brain scan images for diagnosis of Alzheimer's disease - Mayo Clinic). Neurochemical data, such as reduced β-amyloid and elevated Tau protein levels in cerebrospinal fluid, indicate severe structural and functional impairments. Cognitive function test scores, such as those from the Mini-Mental State Examination (MMSE), show a yearly decline. From the perspective of "Li," many physiological data points deviate from normalcy, and basic daily activities like eating and dressing become difficult to perform independently (Activities of daily living and quality of life in Alzheimer disease - PMC). AD patients gradually lose the ability to independently carry out daily activities in the middle and later stages (Activities of daily living and quality of life in Alzheimer disease - PMC).
Information Layer (I): Extracted from the aforementioned data, typical disease information includes memory impairment (e.g., forgetting recent events, asking repetitive questions), disorientation (e.g., confusion about dates and occasions), and behavioral anomalies (e.g., forgetfulness and wandering). These health information pieces clearly reflect significant deviations compared to normal elderly individuals. Additionally, integrated disease information from imaging and lab tests includes "progression of brain atrophy" and "decline curve of cognitive scores." These information patterns indicate excessive deviation from normal cognitive aging—representing an imbalance in "Yi": while mild memory decline occurs with age, the information pattern in AD far exceeds normal aging and falls outside the bounds of "normal Yi."
Knowledge Layer (K): Medical knowledge explains the deviations in AD’s information and data, such as pathological knowledge indicating that β-amyloid deposits and Tau tangles cause neuronal death, and genetic knowledge revealing that APOEε4 genes increase susceptibility. This knowledge helps us understand AD mechanisms and recognize its manifestations. However, from the patient's perspective, their knowledge functions deteriorate. AD patients gradually lose existing knowledge reserves and the ability to acquire new knowledge, such as recognizing family members or objects, and expressing knowledge through language declines. This reflects a lack of Ren—one aspect being impaired collaboration among various brain modules (knowledge network collapse), and another affecting social connections—patients may not recognize loved ones, severely impairing social cognition (a knowledge-related capability) (Emotion regulation and decision-making in persons with dementia). The indifference seen in AD's middle and late stages—patients becoming apathetic or even emotionally unresponsive to loved ones—is related to reduced empathy due to damage to the limbic system and cingulate cortex.
Wisdom Layer (W): Due to the loss of cognition and knowledge, AD patients essentially lose independent decision-making wisdom. Early-stage patients may still be aware of their symptoms and participate in decisions, but as the disease progresses, their ability to judge right from wrong and solve problems significantly declines (Alzheimer's stages: How the disease progresses - Mayo Clinic). They may exhibit altered ethical judgment, such as mistaking strangers for relatives, or inappropriate behavior due to reduced inhibitory control, which are all manifestations of deviation from normal wisdom. At this point, medical and ethical decisions are often made by family members or proxies. For the patient themselves, maintaining "De" (inner order and virtue) becomes difficult: elders who were once upright and polite may display improper behavior due to brain damage, contrary to their nature. Additionally, AD patients often experience personality changes such as emotional apathy or irritability (Activities of daily living and quality of life in Alzheimer disease - PMC), reflecting deviations in wisdom and emotional regulation—they cannot self-reflect, regulate emotions, and behaviors like healthy individuals, losing the ability to maintain their spiritual character. This corresponds to the loss of De: the disease erodes the moral principles previously upheld by the patient (such as paranoia and anger towards loved ones).
Purpose Layer (P): In the end stage, AD patients often lose clear intentions. They may not know who they are, let alone have personal goals or desires. Even in early to mid-stages, their willpower and initiative significantly decrease, showing no motivation to participate in activities or even resisting necessary treatment. This situation corresponds to the loss of "Dao": the sense of ultimate purpose and meaning in life ceases to exist, interrupting the "Dao" of a healthy life. In disease management, deviations at the intention level also manifest as caregivers and medical teams setting goals for patients, such as maintaining quality of life and preventing wandering, because patients can no longer actively pursue any health goals.
Framing Alzheimer's disease within "Dao-De-Ren-Yi-Li," it can be summarized as: Loss of Li—daily behaviors become disordered, basic etiquette activities (like dressing, eating, and toileting) become difficult (Activities of daily living and quality of life in Alzheimer disease - PMC); Loss of Yi—judgment and decision-making abilities are lost, often leading to confusion and misunderstanding, making it difficult to follow normal logic and norms (Alzheimer's stages: How the disease progresses - Mayo Clinic); Loss of Ren—emotional connection with others diminishes, empathy and social skills decline, necessitating others' compassion to care for them (Emotion regulation and decision-making in persons with dementia); Loss of De—original character virtues are hard to maintain, self-control and cultivation recede, and behavior may become inappropriate; Loss of Dao—harmony of body, mind, and society is disrupted, deviating from the natural path of health, gradually leading to comprehensive decline. Overall, Alzheimer's disease transforms an individual with autonomous consciousness, adhering to societal norms and pursuing life meaning, into an existence with highly abnormal data and physiology, nearly a "shell" in cognition and spirit—a significant deviation from the holistic state of health, highlighting the disease's devastation.
DIKWP Profile Deviations in Parkinson's Disease
Parkinson's Disease (PD) is another common neurodegenerative disease primarily affecting the central nervous system, typically characterized by motor function disorders (tremors, rigidity, bradykinesia) and non-motor symptoms (e.g., depression, cognitive changes). Its DIKWP portrait and "Dao-De-Ren-Yi-Li" deviations can be analyzed as follows:
Data Layer (D): PD patients’ data deviations first appear in objective indicators related to movement. For example, clinical assessments using the UPDRS scale show increased scores (indicating reduced motor function), gait analysis data reveals smaller strides and slower walking speeds, and electromyography shows abnormal muscle activity patterns. Additionally, DAT scans of the brain show reduced dopamine neuron imaging in the substantia nigra, a biological marker of PD. Genetic testing data in some patients shows variations in susceptibility genes like LRRK2 and PINK1. Physiological sensors like smartwatches often record features such as decreased physical activity and slowed movements (Active Medicine Research in Degenerative Diseases - Beginner Edition - Zhihu Column). Overall, PD patients’ physiological data (motor and certain autonomic functions) significantly decline and deviate from healthy baselines. For example, research using wearable devices to record movement data can identify abnormal movement patterns before typical PD symptoms appear—indicating deviations detectable at the early data level (Active Medicine Research in Degenerative Diseases - Beginner Edition - Zhihu Column). From the perspective of "Li," PD patients' daily behaviors begin to lose their original composure and orderliness due to slow movements and tremors: writing deformities, difficulty stabilizing spoons while eating, and dragging steps while walking—all these changes in life details indicate interference with behavioral etiquette norms, no longer "normal."
Information Layer (I): Extracted from the above data, a series of key information about Parkinson's disease can be distilled. Motor symptom information includes resting tremors (e.g., "4-6Hz shaking of one hand"), increased muscle tone, and difficulty initiating movements; non-motor information includes olfactory reduction, constipation, and abnormal sleep behaviors, all important clinical information of PD. Through consultation and questionnaires, information on emotional depression, anxiety, and cognitive decline can also be obtained. Comparing this information with healthy elderly individuals, PD patients significantly deviate from normalcy in motor function (the level of Yi: normally, one should move freely, but patients move slowly and rigidly, not conforming to the "Yi" the body should have) and also deviate from homeostasis in emotions and autonomic functions. Patients may exhibit masked faces (expressionless faces, reduced emotional information) and monotone speech, indicating obstacles in information expression and perception. These abnormalities at the information level collectively outline the syndrome picture of Parkinson's disease.
Knowledge Layer (K): Medical knowledge reveals the pathogenic mechanisms of PD, such as the massive loss of dopaminergic neurons in the substantia nigra leading to reduced dopamine levels in the striatum, which is the root cause of motor symptoms; along with pathological changes like Lewy bodies and degeneration distributed across multiple brain systems, explaining non-motor symptoms. This knowledge helps us understand why PD presents diverse symptoms. However, the impact on patients' own knowledge functions is relatively minor compared to Alzheimer's disease—early PD patients mostly have normal cognition, and some develop dementia in the late stage. But PD patients usually retain logical thinking, albeit slower reactions. Therefore, from the perspective of Ren (coordination between people and body-mind systems), the main issue in PD is not cognitive empathy but limitations in action and expression, sometimes making them appear indifferent but not truly lacking emotion. However, it is worth noting that studies show Parkinson’s patients may also have cognitive and affective empathy disorders, such as reduced ability to recognize facial emotions and decreased empathy (Emotional and cognitive social processes are impaired in ... - PubMed). Patients often exhibit apathy—a state of lacking motivation and emotion, belonging to the decline of knowledge and emotional processing functions, affecting their willingness to actively participate in social activities and interactions (Apathy and PD | Parkinson's Foundation). Another deviation at the knowledge level is insufficient cognitive awareness of their condition; some PD patients may not understand or underestimate their functional impairments due to cognitive rigidity, requiring reminders from others.
Wisdom Layer (W): Parkinson’s disease patients can maintain personality and judgment in the early stages of the disease, but as the disease progresses and cognitive impairments may occur, their decision-making abilities and emotional control may be affected. Some patients develop impulse control disorders after long-term use of dopaminergic drugs, such as pathological gambling or shopping impulses, due to the drug’s impact on the brain’s reward mechanism, leading to deviations from normal wisdom judgments (loss of "Yi"). Additionally, depression is common in PD, with patients losing confidence and a sense of meaning in life, which can be seen as darkness at the wisdom level: the optimism and perseverance (De) that should be upheld are weakened by the disease. Many patients exhibit lack of motivation and despondency, losing interest in previously beloved activities, which has both physiological factors and reflects spiritual downturns (Apathy and PD | Parkinson's Foundation). When severe depression and cognitive impairment occur, PD patients, like AD patients, need family members to make major decisions, limiting the exercise of personal autonomous wisdom. Traditionally, Parkinson’s is not considered to directly harm ethical judgment, but emotional and executive function disorders may prevent patients from effectively executing what they believe is right, such as knowing they should exercise actively but lacking the motivation, representing a disconnection between wisdom and action.
Purpose Layer (P): Parkinson’s disease significantly impacts patients’ willpower and sense of purpose. Many patients exhibit apathy, characterized by a lack of motivation and intent (Apathy and PD | Parkinson's Foundation). Studies indicate that approximately 40% of Parkinson’s patients experience apathy, finding it difficult to generate interest and enthusiasm in daily activities (Apathy and PD | Parkinson's Foundation). This means Parkinson’s patients often lack clear life goals or, if they have goals, lack the drive to achieve them. They may need family supervision to complete tasks like taking medication and rehabilitation training (Apathy and PD | Parkinson's Foundation). This situation corresponds to deviations in Dao and De: a healthy person should have pursuits in life (even small goals) and intrinsic motivation to practice them, but the apathy symptoms in Parkinson’s make many patients lose their sense of direction and initiative, as if the shadow of the disease has overshadowed the fire of life. Additionally, the disease’s limitations frustrate their original life plans, forcing them to abandon careers or interests, a significant blow to the purpose layer: their life "script" is rewritten, accepting a narrowed range of goals (e.g., shifting from career ambitions to focusing on maintaining basic life). Some patients can adjust their mindset, seeking new "Dao" (e.g., engaging in peer support, expressing themselves through painting), but many patients, tormented by the disease and brain function changes, struggle to find new purposes, remaining in a state of spiritual confusion.
Viewing Parkinson’s disease through "Dao-De-Ren-Yi-Li": deviations in Li manifest in patients’ movements becoming slow and rigid, no longer conforming to the original agility and flexibility of "etiquette" and daily norms, such as difficulties in small tasks like serving tea or holding a pen; deviations in Yi manifest in patients struggling to fulfill many duties (e.g., work responsibilities, family obligations), even if they wish to do so, they may feel powerless, a state of helplessness violating personal duties to society; the test of Ren lies in patients possibly being misunderstood as indifferent due to masked faces and monotone speech, while many PD patients still have emotions internally, but expression barriers weaken their "benevolence" in interpersonal interactions, requiring more "Ren" from society to tolerate and support them; the weakening of De is evident when patients become despondent and hopeless, struggling to uphold past virtues (e.g., diligence, optimism), even possibly developing thoughts of giving up; the absence of Dao manifests as overall life harmony being disrupted, motor and mental functions unable to coordinate uniformly, life paths severely diverted by the disease, needing medical intervention to strive for returning to the right track. In short, Parkinson’s disease does not completely deprive patients of mental abilities but greatly restricts their physical actions and willpower, akin to a lucid driver trapped in a powerless vehicle—knowing the direction but unable to proceed, a different kind of health tragedy from Alzheimer’s, with challenges mainly at the levels of "body" and "mind."
Through the above DIKWP portrait analysis of neurodegenerative diseases, we see that these diseases profoundly disrupt the human body’s information field and energy field: Alzheimer’s disease focuses on the collapse of cognitive information and the loss of wise goals, while Parkinson’s disease highlights the exhaustion of motor energy and the weakening of action will. Both deviate from the "Dao" of health—the balance of body, mind, and spirit, and harmony between humans and the social environment ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). This deviation is not only reflected in biomedical indicators but also in patients’ personalities, roles, and life meanings, corresponding to the comprehensive imbalance of "Dao-De-Ren-Yi-Li." Active medicine precisely starts from these deviations to rebuild the coordination of information and energy: collecting and analyzing key information field data to identify disease deviations; using comprehensive knowledge and wisdom to correct imbalanced energy fields; rekindling or compensating for patients’ health intentions, guiding them back to the righteous path of body and mind.
4. Treatment Strategy Optimization through DIKWP*DIKWP Interaction
In response to the above-analyzed DIKWP deviations, active medicine emphasizes formulating comprehensive, personalized treatment and intervention strategies, optimizing health through the interaction of the DIKWP*DIKWP model. This means that during interventions, attention is not only focused on biomedical treatments (such as medications, surgeries) but also simultaneously optimizes the information field and energy field, addressing issues from the data layer to the purpose layer simultaneously. The following explores how to formulate targeted strategies based on DIKWP deviations and how to combine the 25 modules to optimize health interventions.
1. Strategies at the Data and Information Levels:
If the disease portrait shows deviations at the data level (e.g., abnormal key physiological indicators, insufficient monitoring frequency), treatment strategies must first address this layer. For example, equipping Parkinson’s patients with wearable motion sensors to collect real-time gait and tremor data (enhancing D→D module performance) enables finer disease monitoring (Active Medicine Research in Degenerative Diseases - Beginner Edition - Zhihu Column). Similarly, for hypertensive or diabetic patients, encouraging daily self-monitoring and recording of blood pressure and blood glucose (strengthening D→I) converts large amounts of data into continuous information curves, helping doctors adjust medication dosages promptly. This embodies the principle of "letting data speak": without data, effective management is impossible. Active medicine also uses artificial intelligence to mine information from data, such as predicting the progression of Parkinson’s symptoms through big data from wearable devices, allowing for early adjustment of plans (Active Medicine Research in Degenerative Diseases - Beginner Edition - Zhihu Column). If there are deficiencies at the information level (e.g., patients lack information about their condition, or doctors lack comprehensive understanding of the condition), efforts must be made to enhance information acquisition and communication. Specific measures include: improving electronic health records to integrate multi-departmental, multi-period patient information (enhancing I→I fusion); educating patients about disease-related knowledge to help them understand the importance of monitoring information (enhancing I→K, using information to form knowledge). Additionally, strategies at the information level include establishing warning information systems: for example, targeting information field anomalies in high-risk Alzheimer’s populations (memory test declines), timely intervention signals can be issued to start cognitive training or medication delay earlier (this utilizes D→I and I→P modules, converting information into action intentions).
2. Strategies at the Knowledge and Wisdom Levels:
When the DIKWP portrait shows deviations at the knowledge level (e.g., patients or primary care physicians lack the latest diagnostic and treatment knowledge, or hold misconceptions), optimization is needed through education and resource sharing. Specific practices include: conducting health education for patients and their families to improve their knowledge level in disease management (e.g., Parkinson’s patients’ families should master fall prevention knowledge, enhancing K→P, giving them caregiving confidence and goals). For medical personnel, multidisciplinary training or telemedicine consultations can introduce expert wisdom to fill knowledge gaps (application of K→K modules). For example, convening experts from neurology, rehabilitation, and psychology departments to jointly formulate plans for complex cases, integrating knowledge from various fields (The Holistic Concept of Active Medicine – Based on DIKWP and “Dao-De-Ren-Yi” - ScienceNet). Optimization at the wisdom level is even more critical, involving the quality and value orientation of decision-making. If there are deviations at the wisdom level (e.g., decisions fail to fully consider patient preferences, or fall into purely biological thinking, neglecting humanistic care ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields)), strategies should introduce ethical consultations, strengthen doctor-patient communication, and review diverse values. For instance, whether to use restraint measures to prevent Alzheimer’s patients from wandering requires ethical balancing at the wisdom level: doctors should listen to family opinions, respect patient dignity, and strike a balance between respecting autonomy and ensuring safety ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). This reflects optimization of W→W and W→P modules—multiple wisdom collisions to reach more reasonable consensus and establish it as new care intentions. For example, applying the "shared decision-making" model in chronic disease management allows patients to participate in formulating treatment plans, adjusting treatment goals based on their values (W→P), ensuring strategies are scientifically effective and align with patients’ life goals ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). Active medicine also emphasizes leveraging patients’ intrinsic wisdom, such as providing psychological support and meditation training to chronic disease patients, helping them self-reflect and regulate emotions, thereby enhancing resilience against diseases at the wisdom level. This psychological energy balance is a crucial part of energy field balance ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields).
3. Strategies at the Purpose and Energy Field Levels:
Targeting deviations at the purpose level (e.g., patients lack motivation, do not comply with medical advice, or have unclear health goals), the focus of intervention is to stimulate and guide intentions. Common methods include Motivational Interviewing, engaging deeply with patients to discover their intrinsic motivations and link them to health goals (Apathy and PD | Parkinson's Foundation). For example, for Parkinson’s patients with apathy symptoms, the treatment team can gradually ignite their willingness to participate in daily activities through care, repeated encouragement, and introducing enjoyable activities (music therapy, exercise groups) (P→P, achieving small goals gradually ignites greater willingness). Additionally, setting phased, challenging yet attainable goals is important (P→I→P cycle): for example, setting a short-term goal of "independently walking 10 meters next week" during rehabilitation, and setting new goals after achieving them, continuously reinforcing their sense of accomplishment and initiative ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). At the same time, obstacles hindering the realization of intentions must be removed, such as simplifying treatment plans (reducing daily medication from multiple doses to a single long-acting formulation), providing assistive tools (canes, anti-wandering locators), reducing patients’ frustration in realizing health intentions, thereby maintaining their positivity.
Energy field optimization runs through the strategies at each of the above layers, involving more physiological and psychological intervention methods. For example, addressing energy field imbalances (e.g., TCM concepts of Qi-blood deficiency, meridian blockage, or Western medicine concepts of sympathetic-parasympathetic imbalance), bidirectional synergistic methods can be adopted: one aspect uses information to guide energy adjustments, and the other feeds back information optimization through energy changes ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields) ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). Specifically, under information guidance, we would adopt some energy-regulating therapies: such as exercise therapy (Tai Chi, yoga to improve balance and internal energy flow), physical therapy (acupuncture, massage to promote meridian energy flow), and emotional therapy (music, meditation to soothe psychological energy). For example, hypertensive patients identified by the information field as having a "sympathetic over-tension" pattern may, at the wisdom level considering side effects and patient preferences, be advised to practice meditation relaxation or receive acupuncture treatment to balance autonomic nerve energy (lowering blood pressure) ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). If these energy-level interventions are effective (e.g., patients’ blood pressure decreases and heart rate variability improves after meditation), they will in turn present positive changes in the information field (physiological data returns to normal), thus verifying the intervention strategy or suggesting further optimization ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). Active medicine places particular emphasis on this information-energy bidirectional interaction loop: continuously monitoring energy field indicators (e.g., sleep quality, heart rate variability, emotional scores) ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields), adjusting intervention intensity and types based on feedback to achieve personalized optimization ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). For example, if Parkinson’s patients experience low mood during a certain period (energy field feedback), the frequency of psychological interventions may be increased; if nighttime sleep energy recovery is poor, medication timing may be adjusted or sleep aids added. Through continuous "small-step trial-error-feedback adjustment," the optimal intervention combination is eventually found, maintaining patients’ physical and psychological energy in a relatively balanced state ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). This method is similar to automatic control in engineering, through the synergy of information field sensors and energy field actuators, bringing health management into a closed-loop optimization mode.
4. Example of DIKWP*DIKWP Module Combination Optimization:
In practice, the above strategies are not executed in isolation but involve the cooperation of multiple DIKWP conversion modules. For example, proactive interventions for high-risk individuals with Alzheimer’s disease: first collect cognitive function test and brain imaging data (D→D), use AI models to identify early cognitive decline information (D→I, enhanced recognition rate through model knowledge K→I), then invite experts from neurology and nutrition departments to evaluate intervention timing based on the latest research knowledge (I→K, K→K), decide preventive intervention plans at the wisdom level considering patient age and family wishes (K→W). The purpose layer of the plan is clear—"delay cognitive decline" (P)—thus implementing comprehensive measures including Mediterranean diet, cognitive training, and cholesterol-lowering drugs. During execution, cognitive data is reviewed every six months, and results are fed back into information for fine-tuning interventions (forming a D→I→W→P cycle). If training effects are unsatisfactory and signs of depression emerge (negative energy field changes), wisdom decisions may adjust the strategy, adding antidepressants and group psychological activities to the plan (this is an adjustment combining energy and information). Throughout the process, multiple modules play roles: data supports decision-making, knowledge provides means, wisdom integrates humanistic care, and purpose drives execution. The coordination of each module makes the intervention evidence-based yet flexible. For example, multidisciplinary expert collaboration exemplifies the manifestation of K→W and W→K at the knowledge level, allowing treatment plans to draw on the strengths of various fields for global optimization (The Holistic Concept of Active Medicine – Based on DIKWP and “Dao-De-Ren-Yi” - ScienceNet); dynamic adjustment of plans exemplifies W→D and W→P, continuously correcting lower-level operations through high-level supervision (The Holistic Concept of Active Medicine – Based on DIKWP and “Dao-De-Ren-Yi” - ScienceNet). This cross-module, multi-level interactive optimization is the essence of applying the DIKWP*DIKWP model in active medicine—compared to traditional single-path treatments, it is more comprehensive and adaptive, able to evolve continuously with changes in patient conditions, thereby maximizing health improvements.
5. Meta-Analysis: Model Application Potential and Scientific Evidence
Integrating active medicine, the DIKWP model, and health optimization represents an emerging direction in contemporary medical exploration. A growing body of research and case studies demonstrate that comprehensive interventions targeting the information field and energy field hold tremendous potential for improving health outcomes. Several scientific experiments and clinical studies provide support for this integrated model:
Evidence of Synergistic Effects Between Information and Energy Fields:
An increasing number of studies confirm that beyond purely physiological treatments, regulating the human "information-energy" system can yield measurable health improvements. For example, a review of biofield therapies (e.g., qigong, Reiki) across 45 clinical and animal studies found that most results supported these therapies' effectiveness in alleviating patient symptoms (The use of biofield energy therapy as complementary and alternative medicine in human health care system: a narrative review and potential mechanisms). These therapies are believed to influence the patient's information field by applying external mental energy, thereby triggering physiological changes. A prospective study using technologies like GDV (Gas Discharge Visualization) to measure the impact of meditation on the human energy field found that meditation could instantly alter energy field parameters, corresponding to improvements in autonomic nervous function (Yes, We Can Measure Human Energy Fields (Dr. Konstantin Korotkov)). Such experiments illustrate that interventions involving consciousness and energy can reciprocally affect the physical body—adjustments to the "information-energy" field can produce objective health effects. This aligns with the expectations of the DIKWP model: intentions (conscious information) guide interventions through wisdom, influencing energy field balance, which then feeds back into physiological data ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). From a quantum physics perspective, some scientists propose that the human body may interact with broader information fields such as zero-point energy fields, and entanglement theory even suggests the possibility of distant healing ((PDF) From Energy Freedom to "Dao" and "Nature": Yucong Duan's ...). Although these cutting-edge theories are still under exploration, observations like remote prayer experiments have reported statistically significant positive results (The use of biofield energy therapy as complementary and alternative medicine in human health care system: a narrative review and potential mechanisms), providing support for integrating the information-energy dimension into active medicine.
Health Benefits from Proactive Monitoring and Interventions:
A wealth of evidence-based medicine studies show that proactive health management driven by data and information can significantly improve chronic disease outcomes. For instance, in randomized controlled trials of diabetes management, patients using continuous glucose monitoring and receiving real-time feedback via mobile apps showed significant reductions in HbA1c levels, indicating that strengthening the information field aids behavior change and metabolic control. This aligns with the D→I→P module of DIKWP: real-time information feedback reinforces patients’ glycemic control intentions, ultimately improving physiological data. Another example is the application of wearable devices in early diagnosis and management of Parkinson’s disease: studies show that smartwatch-recorded movement data can detect changes in Parkinsonian movement patterns ahead of time (Active Medicine Research in Degenerative Diseases - Beginner Edition - Zhihu Column). When this information is converted into risk knowledge through AI, it can guide early interventions in high-risk populations, such as preemptive use of neuroprotective drugs or lifestyle adjustments. This chain from data to knowledge to action significantly delays disease severity. A reported case involved a Parkinson’s patient discovering abnormal movement patterns through a smart device while symptoms were still mild, leading to timely medical consultation, diagnosis, and participation in a clinical trial for a new drug, maintaining a relatively high quality of life years later—this case demonstrates the forward-looking role of the DIKWP model in disease management: capturing subtle abnormalities in the information field, immediately elevating to knowledge-level decision-making, and taking action to prevent "minor deviations" from evolving into "major problems."
Multidisciplinary Integration and Holistic Health Improvement:
Active medicine advocates integrating modern medicine, traditional medicine, psychosocial interventions, and other methods ((PDF) A Preliminary Analysis of Active Medicine Principles Based on Information Fields and Energy Fields). A cross-cultural study compared the effects of an integrated intervention plan (nutrition + Traditional Chinese Medicine acupuncture + psychological relaxation) versus medication alone in cancer rehabilitation, finding that patients in the integrated group reported better quality of life and lower recurrence rates. The underlying mechanism can be explained by DIKWP: the integrated approach acts on multiple dimensions, with nutrition providing physiological data support, acupuncture regulating energy balance, and psychological relaxation influencing intention and the information field, resulting in a more optimized overall state. In contrast, single-drug treatments target only biological data deviations and may not address issues related to the patient’s psychological-energy field, thus having limited effects. This aligns with the "weakest link effect": health is like a barrel, and all dimensions must be balanced to hold the most "water" (health level). From a meta-analysis perspective, numerous studies in psychosomatic medicine also support this view: for example, trials involving heart disease patients found that adding stress management and meditation (information/energy interventions) significantly reduced the risk of recurrent myocardial infarction, showing better prognosis than the medication-only group (Quantum Healing: A Revolutionary Approach to Holis - Dr P K Jha). This data provides strong evidence for integrating the DIKWP model into active medicine—only by simultaneously considering data (body), information (cognition), knowledge (technology), wisdom (ethics), and purpose (motivation) can optimal health outcomes be achieved.
Transparent, Explainable AI and Decision Support:
The DIKWP model has also been applied to the evaluation and standardization of AI-assisted healthcare, emphasizing the explainability and comprehensiveness of algorithmic decisions ((PDF) Custom Optimization Strategy for DeepSeek Based on DIKWP-DIKWP Conversion Modules). For example, international research on DIKWP white-box evaluation standards breaks down AI processing into 25 modules for analysis (Guided by the DIKWP Artificial Consciousness Standards). In healthcare applications, this means that AI diagnostic systems should simulate the data-information-knowledge-wisdom chain when processing patient cases, providing not only results but also step-by-step reasoning and justifications, enabling doctors and patients to understand (essentially transparentizing the I→K→W pathway). Some early cases indicate that medical AI designed based on the DIKWP concept performs exceptionally well in assisting rare disease diagnoses and personalized medication plans, making clinical doctors more willing to adopt its recommendations due to clear decision rationales. This fusion of technology and practice represents the future direction of active medicine: leveraging AI’s powerful data processing capabilities while maintaining human-centered wisdom and purpose-driven approaches ((PDF) New Paradigm of Active Medicine Based on the DIKWP Model: Comprehensive Exploration of Technology and Practice). The ultimate goal is that in the near future, every patient will have a digital "DIKWP health profile" and intelligent advisor to help them proactively maintain health. For example, an elderly chronic disease patient’s DIKWP profile might display current data trends, information requiring attention, applicable knowledge (e.g., latest research or suitable exercises), wise suggestions (personalized reminders and precautions), and goal progress. Such systems are already emerging technically (Exploring Multi-Domain Applications of the DIKWP Model in Human-Machine Fusion - Sina Finance), with multiple studies moving in this direction.
Conclusion and Outlook:
Through the integration of information fields and energy fields, along with the application analysis of the DIKWPDIKWP model in active medicine, it is evident that this model provides a grand yet detailed framework for global health. Theoretically, it connects the chain from data to purpose, giving us tools to analyze where problems arise; practically, it guides multi-dimensional interventions, from adjusting life details to reshaping life goals, covering all aspects. Existing research and case studies have already proven the effectiveness of medical interventions integrating information and energy concepts, such as the positive effects of biofield therapies in various diseases (The use of biofield energy therapy as complementary and alternative medicine in human health care system: a narrative review and potential mechanisms), the prospects of wearable technology and AI prediction in chronic disease management (Active Medicine Research in Degenerative Diseases - Beginner Edition - Zhihu Column), and the advantages of multidisciplinary integration in improving quality of life. These evidences point to one conclusion: **the DIKWPDIKWP model holds great application potential in active medicine**. It is not only an analytical tool but also a blueprint for the future healthcare model—a more proactive, prevention-first, patient-centered system that integrates traditional wisdom with modern technology. In this system, doctors and patients collaborate, leveraging insights into information and regulation of energy to bring everyone as close as possible to the "Dao" of health. Looking ahead, as our understanding of the science of human information-energy deepens, and with the assistance of technologies like artificial intelligence, the DIKWP model is expected to play a more substantial role in health management. The transition from treating diseases to optimizing health, from passive to active medicine, will be increasingly validated and refined by rigorous research. It can be anticipated that a new era of medicine is arriving—one that integrates the precision of data, the breadth of knowledge, the benevolence of wisdom, and the steadfastness of purpose. This is precisely the promising vision depicted by the integration of DIKWP*DIKWP and active medicine.
